Review follow-up (Khalim): the first pass made far more optional than needed —
notably the whole SapBuildingPart block — and a buggy 21.0.0↔21.0.1 diff also
MISSED open_chimneys_count / cfl_/led_fixed_lighting_bulbs_count / suggested_
improvements, so the original change actually mapped only 3 of the 33 skipped
certs (the rest still failed on open_chimneys_count).
Re-derived the exact set empirically from all 33 skipped cohort certs:
widen only fields that are (a) required in 21.0.0, (b) already optional in
21.0.1, AND (c) genuinely omitted by ≥1 of those certs. Result:
- KEEP optional: the 4 SapWindow refinements, the top-level vent/lighting/
door/pressure-test block (incl. the 3 previously-missed fields), 2
SapEnergySource fields, Addendum.addendum_numbers, PhotovoltaicSupply.
none_or_no_details, and exactly ONE building-part field
(SapBuildingPart.roof_insulation_thickness — omitted by 7 certs).
- REVERT to required: the other 12 SapBuildingPart fields (construction_age_
band, wall_construction, …), MainHeatingDetail.emitter_temperature,
PvBatteries.pv_battery, ShowerOutlets.shower_outlet — none of the 33 certs
omit these, so they stay strict.
Mapper: coalesce the count fields (wet_rooms_count, open_chimneys_count,
cfl_/led_fixed_lighting_bulbs_count) to 0 like every other mapper, so the now-
optional values can't reach a NOT-NULL column (also drops 4 pyright ignores).
Now maps 32/33 (up from 3); the last cert hits a pre-existing pv_batteries-
shape AttributeError and degrades via the ADR-0031 skip path. pyright net
unchanged (43, no new errors); regression test rewritten to the real omitted set.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
PostgresUnitOfWork built its PropertyPostgresRepository without an overrides
reader, so a Property re-hydrated through the unit silently dropped its
Landlord Overrides (ADR-0032). The Baseline orchestrator runs through the UoW,
so it scored the bare lodged EPC while the Plan modelled the override-folded
Effective EPC — the two diverged (e.g. baseline effective 71/C vs plan
baseline 62/D), producing "already at band C yet recommends reaching C".
Wire PropertyOverridesPostgresReader into the unit's property repo (uow-
independent committed reference data, read via the same session factory) so
every re-hydration folds overrides, matching the live modelling path.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Fitting sealed glazing units changes two things beyond the pane's U/g
that the cascade reads, which the overlay didn't model — leaving the
double/secondary before→after pins ~0.7 SAP short (xfail):
1. Draught-proofing (RdSAP 10 §8.1). Sealed units draught-proof the panes
they replace, re-lodging the dwelling-level `percent_draughtproofed`
(cert 001431: 84 → 100). The §2 cascade reads that dwelling-level
value, so the overlay now carries it. `_recompute_percent_draughtproofed`
anchors on the lodged before-% — `after = round((round(before%/100 × N)
+ flips) / N × 100)`, N = openable windows (vertical + roof) + doors,
flips = upgraded panes that were not draught-proofed — so it's robust
to incomplete window extraction (unchanged openings are already in the
aggregate). ~0.3 SAP.
2. Frame factor (§6 solar gains). A replacement unit re-lodges its own
FF=0.70, overriding the pane it replaced — the two "single glazing,
known data" panes lodge FF 1.00 / 0.50 (one is 6.6 m²), so leaving them
unchanged understated solar gains by ~+150 kWh space heating. `WindowOverlay`
now carries `frame_factor`, written flat onto the window. ~0.4 SAP.
Wiring: `EpcSimulation.percent_draughtproofed` + `WindowOverlay.frame_factor`
new fields; `apply_simulations` / `_fold_window` write them; the glazing
generator computes both from the upgraded set and cert 001431's after.
Un-xfails `test_{double,secondary}_glazing_overlay_reproduces_the_relodged_after`
— both now pin SAP/CO2/PE to the relodged after within tolerance. Updates
the two `test_glazing_recommendation` overlay expectations for the new
`frame_factor`. 96 modelling tests pass; zero new pyright errors.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Comment out the remaining workflows to cut GitHub Actions usage, per request:
- integration_tests.yml — rebaselining integration suite (PRs to main)
- deploy_fastapi_backend.yml — FastAPI backend deploy (push to dev/prod);
deploys must be run manually via `sls deploy` while disabled
- protect_releases.yml — main→dev PR-source guardrail
Fully commented (not deleted) so each restores by uncommenting.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Comment out the Docker-based unit-test workflow — it was consuming too many
GitHub Actions minutes. Fully commented (rather than deleted) so it can be
restored by uncommenting.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
37 modelling_e2e properties failed on the 2026-06-23 run with
`NotNullViolation: null value in column "wet_rooms_count" of relation
"epc_property"`.
Root cause: 21.0.1 lodges `wet_rooms_count` as Optional, and
`from_rdsap_schema_21_0_1` passed it straight through
(`wet_rooms_count=schema.wet_rooms_count`). A cert omitting it mapped to
`EpcPropertyData.wet_rooms_count=None`. When a predicted EPC (which deep-copies
a comparable template's EpcPropertyData) inherited that None and was persisted,
it violated the `epc_property.wet_rooms_count` NOT-NULL column — and the calc's
`wet_rooms_count > 0` check would also raise `TypeError` on None.
Fix: coalesce to 0, matching every other mapper (RdSAP "not lodged" → the
calc's minimum 1 wet room). Regression test added.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The modelling_e2e cohort skipped ~35 real RdSAP-Schema-21.0.0 certs with
"SapWindow: missing required field 'frame_factor'" (and a cascade of
wet_rooms_count, the mechanical-vent duct block, wind_turbine_details, &c.).
These are complete certs — the `RdSapSchema21_0_0` dataclass was simply
modelled stricter than `RdSapSchema21_0_1`, which already treats every one of
these fields as optional. This aligns 21.0.0's optionality with 21.0.1 (the
proven path) so the certs map into the prediction donor pool instead of being
dropped:
- SapWindow: pvc_frame / glazing_gap / frame_factor / window_transmission_details
→ Optional (calc already falls back: Table 6c frame factor, SAP10 glazing
lookup, type-only U).
- 20 nested fields across Addendum / MainHeatingDetail / PhotovoltaicSupply /
PvBatteries / SapBuildingPart / SapEnergySource → Optional, mirroring 21.0.1
verbatim (classes made kw_only so defaults sit in place; parsed only via
from_dict, keyword construction).
- from_rdsap_schema_21_0_0: guard the window_transmission_details dereference
for None (mirrors the 21.0.1 `_api_sap_window` guard).
Existing 21.0.0 certs are unaffected (relaxing optionality does not change
parsing of certs that carry the fields); mapper-corpus + accuracy gates green.
Verified end-to-end against real cohort cert 2205-3036-3484-0400-5718:
maps + calculates (SAP 68). Regression test added.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
SAP 10.2 Table 12a Grid 2 (PDF p.191) bills "Fans for mechanical ventilation
systems" at 0.71 (7-hour) / 0.58 (10-hour), distinct from "All other uses"
(0.90 / 0.80) which covers circulation pumps, flue fans and the solar HW pump.
The cost-split mech-vent kWh (`mev_kwh_for_cost_split`) only summed the
decentralised-MEV (230b) fans, not the (230a) MVHR fan electricity — even
though the total pumps/fans bucket adds both. So an MVHR dwelling on an
off-peak tariff billed its fan electricity at the 0.90/0.80 "all other uses"
rate instead of 0.71/0.58. The comment already said "MEV/MVHR-fan portion";
only the MEV term was wired when MVHR landed. Fixed to mirror both
mechanical-ventilation fan terms summed into the total.
Worksheet-proven on simulated case 50 (000565 semi + MVHR Vent Axia + dual
electric immersion, Unknown meter -> 7-hour via the §12 dual-immersion
trigger): the fan bucket (315.64 kWh, 100% MVHR per worksheet line 230a) was
billing at 14.311 p/kWh (0.90) vs Elmhurst's 12.451 p/kWh (0.71) — +£5.87/yr,
-0.23 SAP. After the fix our existing-dwelling rating reconciles to Elmhurst
EXACTLY: SAP value 38.8426 (=Elmhurst 38.8426 -> 39), total cost £1317.0116
(=Elmhurst £1317.0116 to the penny).
Same `mev_kwh_for_cost_split` feeds the CO2 + PE cascades, so all three split
consistently. 0 corpus impact (all 3 corpus MVHR certs are standard tariff);
gauge unchanged 73.3% / MAE 0.774 / CO2 0.08 / PE 3.4.
Pin: test_mvhr_fan_electricity_bills_at_grid2_fan_fraction_on_off_peak.
pyright strict gate not run locally (pyright not installed in this container).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
SAP 10.2 Table 4a electric boilers (PDF p.170) split across three distinct
Table 12a Grid 1 SH rows (PDF p.191), not one "direct-acting" family as the
stale TODO in `_table_12a_system_for_main` implied:
- 191 Direct-acting electric boiler -> "Direct-acting electric boiler (a)"
row: 7-hour 0.90, 10-hour 0.50 (NOT the 1.00/0.50 "Other direct-acting
electric heating" room-heater row).
- 193/194/195/196 Electric dry core / water storage boiler -> "Electric dry
core or water storage boiler" row: 7-hour 0.00 (charged wholly off-peak =
100% low rate, identical to the None fallback).
- 192 Electric CPSU -> Appendix F; left falling through to None (off-peak
low) until the Appendix-F high-rate cascade is implemented.
The enum + fractions already existed in table_12a.py; only the code->enum
mapping was missing. Resolves the TODO and pins the spec-correct 0.00 for the
storage boilers so 195 can't be mis-"fixed" up to a direct-acting fraction.
Forward guard, 0 corpus impact: storage boilers already billed 100% low via
the None fallback, and all corpus 191 certs are on standard tariff (Table 12a
off-peak split never fires). Corpus gauge unchanged 73.3% / MAE 0.774.
Pin: test_electric_boilers_191_195_map_to_distinct_table_12a_grid1_rows.
pyright strict gate not run locally (pyright not installed in this container).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
SAP 10.2 Table 3a (PDF p.160) additional combi loss (61)m. Two coupled
defects, both surfaced by simulated case 49 (000565 + gas combi, U985
"Combi keep hot type = None") sitting at SAP 71.43 vs the worksheet's 72:
1. The cascade defaulted EVERY non-PCDB combi to the flat keep-hot
time-clock row (600 × n/365). A combi WITHOUT a keep-hot facility uses
row 1 (600 × fu × n/365, fu = V_d/100 when daily HW < 100 L/day) —
over-counting (61)m for the no-keep-hot cohort. `water_heating_from_
cert` now defaults to the "without keep-hot" row.
2. `pcdb_combi_loss_override` returned None for keep_hot_facility=1/
timer=1, leaning on the OLD flat-600 default. So flipping the default
silently turned 190 corpus PCDB keep-hot-time-clock combis into
no-keep-hot. Fixed to return the flat keep-hot row EXPLICITLY.
Key insight (the Summary is the input echo; the U985 keep-hot line is a
computed OUTPUT, so it must be derivable): keep-hot rides on the PCDB
boiler record (Table 105 keep_hot_facility/timer), resolved by
`pcdb_combi_loss_override`. A generic SAP-code combi with no PCDB record
(case 49, PCDF ref 0) has no keep-hot by construction → row 1. So the
default is not a guess — it is the spec-correct value for non-PCDB combis.
Worksheet-proven: case 49 → cost £726.696, SAP 72 — matching the
accredited worksheet to the digit (continuous 71.6945 = the worksheet's
own 71.6945). 000516 (keep-hot None) also exact (£860.716, SAP 63);
000490 (PCDB 10328, keep_hot_facility=1/timer=1) keeps its flat-600 via
the PCDB path. Masked until now because every prior combi-loss worksheet
fixture was keep-hot (000490/000474/000480 time-clock) or had V_d >= 100
every month (001431, rows coincide); case 49 is the first no-keep-hot one.
Corpus within-0.5 72.7% -> 73.3%, MAE 0.781 -> 0.774, PE 3.5 -> 3.4;
ratcheted _MAX_SAP_MAE 0.785 -> 0.775, _MAX_PE_PER_M2_MAE 3.6 -> 3.5.
Note: pyright strict type gate not run locally (pyright not installed).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Extends the dMEV intermittent-fan fix (4db05e84) to MVHR. A balanced
whole-house MVHR system IS the dwelling's ventilation, so the lodged (7a)
intermittent-extract-fan count is explicit — a lodged 0 means 0, not the
RdSAP 10 Table 5 age-band "unknown" default. The cascade was substituting
the default (here 20 m³/h) into worksheet line (8) openings, inflating
(16/18) infiltration → (21) → (22b) → (25) effective ach → (38)
ventilation heat loss → the space-heating demand.
Worksheet-proven on simulated case 49 (000565 + Vent Axia 500140 MVHR,
lodged (7a)=0): our (8) openings 0.0723 -> 0.0000, (18) 0.7223 -> 0.6500,
(25)m Jan 0.9423 -> 0.8571, all now matching Elmhurst exactly; space-
heating demand 7857 -> 7528 kWh (worksheet 7546). SAP 70.90 -> 71.43
continuous. (The residual to the worksheet's 72 is its own continuous SAP
71.69 rounding up, driven by a separate gas-combi water-heating-loss gap,
not ventilation.)
Scoped to EXTRACT_OR_PIV_OUTSIDE + MVHR only — MV-without-HR
(mechanical_ventilation=1) stays on the default-substitution path
(forcing its lodged 0 regressed 47 Howsman / 18 Jutland and is not
worksheet-validated). Corpus within-0.5 holds 72.7%, MAE 0.782 -> 0.781.
Note: pyright strict type gate not run locally (pyright not installed).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
In-container Playwright runs were flaky: the chromium renderer crashed
mid-build ("Target crashed") on the 64M /dev/shm, and login intermittently
hung. Added `--disable-dev-shm-usage` + `--no-sandbox` launch args, a
4-attempt login retry loop (domcontentloaded + explicit selector wait),
and an `ELM_GUID` env override so a per-UPRN assessment can be targeted
without editing the module. Tooling only — no calculator impact.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
MVHR (24a) heat-recovery support, part 2: the mapper + cascade wiring.
Both source paths now resolve balanced whole-house MV with heat recovery
to the MVHR kind:
- gov-API: `_API_MECHANICAL_VENTILATION_TO_KIND` 4 → "MVHR" (was None /
treated as natural — under-stated ventilation heat loss, over-rating).
- Elmhurst Summary: `_ELMHURST_MV_TYPE_TO_KIND` "Mechanical ventilation
with heat recovery (MVHR)" → "MVHR" (was UnmappedElmhurstLabel, which
blocked the whole Summary for MVHR dwellings).
cert_to_inputs resolves the in-use heat-recovery efficiency + SFP for an
MVHR cert (`_mvhr_system_values`): pick the PCDB Table 323 data point by
the lodged wet-room count (SAP 10.2 §2.6.4), multiply the raw efficiency
by the Table 329 ducts-inside-envelope in-use factor (0.90) and the raw
SFP by the per-duct-type factor (rigid 1.4), and feed:
- the §2.6.6 eq (2) effective-air-change credit (23c) → (24a)/(25)m;
- the (230a) fan electricity (in-use SFP × 1.22 × V), costed but NOT
added to the Table 5a gains (its effect is in the efficiency).
An MVHR lodged with no PCDF index falls back to the SAP 10.2 Table 4g
default (raw efficiency 66% × 0.70, raw SFP 2.0 × 2.5).
Worksheet-proven on simulated case 49 (000565 semi + Vent Axia Sentinel
Kinetic B 500140 + gas combi → Elmhurst Current SAP 72): every MVHR line
matches Elmhurst exactly — (33) fabric heat loss 100.5923, (23c) in-use
efficiency 81.9% = 91 × 0.90, (25)m Jan 0.8571, (230a) fan electricity
415.9325, (231) total pumps/fans 501.9325. The residual SAP 71 vs 72 is
the known 000565-family space-heating-demand artifact (same -1/-2 seen on
cases 47/48), not the MVHR logic.
Corpus: within-0.5 72.6% -> 72.7%, MAE 0.788 -> 0.782, PE 3.6 -> 3.5.
The 3 gov-API MVHR certs: Flat 1 +6 -> 0 (Table 4g default path) and
12a Princes Gate +3 -> +1 (heat-recovery credit); Apartment 707 -4 -> -6
is a separate baseline under-rate (it under-rated as natural too — the
MVHR credit correctly adds ventilation loss per Elmhurst's method).
Ratcheted _MAX_SAP_MAE 0.79 -> 0.785, _MAX_PE_PER_M2_MAE 3.7 -> 3.6.
Note: pyright strict type gate not run locally (pyright not installed).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
MVHR (24a) heat-recovery support, part 1: the PCDB data layer.
PCDB Table 323 (PCDF Spec Rev 6b §A.18, Format 426; pcdb10.dat carries
Format 431, header `$323,431,...`) holds the per-wet-room SFP + heat-
exchanger efficiency for centralised MEV / MVHR units. Added
`MvhrRecord` / `MvhrDataPoint`, `parse_centralised_mv_row` /
`parse_table_323`, the ETL step, the committed jsonl, and the
`mvhr_record(pcdb_id)` runtime lookup (mirrors Table 322).
SAP 10.2 §2.6.4/§2.6.6: "MVHR ... SFP is a single value depending on the
number of wet rooms" — each test group's leading field is the wet-room
count; callers select the group matching the dwelling lodgement.
Worksheet-proven on simulated case 49 (000565, 2 wet rooms, Vent Axia
Sentinel Kinetic B 500140 → flow 21.0, SFP 0.88, efficiency 91%).
Also decoded the MVHR heat-recovery efficiency in-use factor from Table
329 (Format 432): system_type 3 ducts-inside-envelope = 0.90 (case-49
(23c) = 91 × 0.90 = 81.9%), cross-checked against system_type 10 = 0.70
(= SAP 10.2 Table 4g default heat-recovery in-use factor). "Table 4h is
no longer used – data now stored in the PCDB" (SAP 10.2 p.176).
The outside-envelope efficiency columns + with-scheme SFP blocks are
preserved verbatim in `raw` (no fixture exercises them yet).
Note: pyright strict type gate not run locally (pyright not installed).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Skipped cohort certs were previously surfaced only as outputs.result on a
completed subtask, so they were easy to miss. Treat them as a failure too:
once the batch has run to completion (so every modellable property is already
written to DB), raise if there were any per-property errors OR any skipped
certs. The run gets flagged for debugging without discarding the work done.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
When a property failed, the handler recorded only its bare property_id and
raised RuntimeError("failed property_ids: [...]"). That string is what
SubTask.fail persists into the subtask outputs.error column, so a failed run
told you which property failed but never why — forcing a CloudWatch lookup.
The per-property catch now captures property_id, uprn, error_type, and the
error message, and the raised RuntimeError embeds those as JSON so the subtask
outputs column is parseable directly. query_failed_modelling_e2e.py reads that
outputs.error into a new Error column in its report.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
A predicted Property (no lodged EPC) got a Plan but nothing else: the synthesised
EPC was never written to epc_property, and Baseline Performance was skipped — so
property 729529 (portfolio 796 / scenario 1268), predicted from its DA16 1QZ
cohort, was "missed" with no predicted-EPC row and no baseline row.
Persist the synthesised EPC in the predicted slot (uow.epc.save(..., source=
"predicted"), ADR-0031) inside the Plan UoW, then run the Baseline orchestrator
for predicted Properties too — it re-hydrates the predicted EPC and establishes
the baseline from it. The earlier "lodged only" guard is dropped: by the write
block the Property always has a persisted EPC (lodged or predicted); one that
could be neither fetched nor predicted raised earlier.
Verified against the DB by invoking the real handler for 729529: predicted
epc_property rows 0->1 and property_baseline_performance rows 0->1. Baseline on
the predicted picture builds cleanly (RHI present, reason pre_sap10). Tests
updated: prediction + broadening paths now assert the predicted-slot epc.save and
the baseline run.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The handler wrote epc/spatial/solar/plan and marked the property modelled, but
never established its Baseline Performance — so no row was created in
property_baseline_performance for any property modelled through the Lambda
(noticed on portfolio 796 / scenario 1268 / property 727218, a lodged property).
Mirror the e2e runner: after the plan UoW commits (so the EPC is persisted for
the orchestrator to re-hydrate), run PropertyBaselineOrchestrator for lodged
properties. Predicted properties have no lodged figures and no persisted EPC, so
they are skipped — consistent with the e2e runner and the ara_first_run Baseline
stage.
Verified 727218's baseline pipeline builds end-to-end in-memory (lodged_performance
→ CalculatorRebaseliner → bill → PropertyBaselinePerformance, reason pre_sap10).
Tests: lodged path asserts the orchestrator runs once; prediction path asserts it
does not.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
A Landlord Override's building_part is a positional index (0=main, 1=extension
1…, ADR-0004), but the gov-API EPC can label that slot differently (e.g. lodge
the 2nd part as 'other', not 'extension_1'). The previous fix skipped such
orphaned overrides, silently discarding the landlord's correction. Now the
override falls back onto the EPC's part at that position (via _resolve_part), so
the correction lands; only a position the EPC models no part at is skipped
(no geometry to model a wholly-absent part). Replaces the skip-only behaviour.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Closes out the cohort-broadening work with its decision record and consolidates
the retry plumbing.
ADR-0034 documents broadening the EPC-Prediction cohort to the real unit
postcodes nearest the target (via postcodes.io) when its own postcode holds no
same-type comparable — extending ADR-0031 decision 5. Records why postcodes.io
was chosen over council[] (whole-LA, no property_type in rows), a bulk Code-Point
Open / ONSPD dataset, and the OS Places radius API, and the lazy / nearest-first
early-stop / soft-fail policy. Broadening-specific docstrings now cite 0034.
Retry consolidation: extract the EPC client's call_with_retry into a shared
infrastructure/http_retry.py keyed off a generic TransientHttpError marker, so
the mechanism (exponential backoff, Retry-After) is shared while each client
keeps its own transient policy. EpcRateLimitError now subclasses TransientHttpError
(still an EpcApiError); PostcodesIoClient routes through the same helper, raising
TransientHttpError on 429/5xx and soft-failing to the seed once exhausted (the EPC
client propagates instead). Direct tests for the shared helper; EPC + postcodes.io
suites repointed at the shared sleep.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Two reconciliations to make the modelling_e2e Lambda handler production-ready.
1. Price through the off-catalogue overlay, drop the workarounds
The handler priced through a plain ProductPostgresRepository and excluded
secondary_heating_removal / system_tune_up / system_tune_up_zoned to dodge
ProductNotFound (and a poisoning pgEnum DataError). Those measures are now
priced by catalogue_with_off_catalogue_overrides (already used by the e2e
runner and PostgresUnitOfWork), so the exclusions are removed and ALL measure
types are considered. This also fixes gas-boiler / single-glazed properties,
which Dan's handler never excluded and so still crashed (the standard
system_tune_up option is built unconditionally — the considered-measures
exclusion never actually gated it).
2. Broaden the EPC-Prediction cohort to nearby real postcodes (ADR-0031)
A property with no lodged EPC and no same-type comparable in its own postcode
(e.g. the only flat among houses) used to gate out and fail the subtask. The
gov EPC API cannot search by radius/outcode, so we resolve the real unit
postcodes physically nearest the target via postcodes.io (keyless; already a
trusted in-repo dependency) and walk them nearest-first until enough same-type
comparables surface. New PostcodesIoClient (transient-failure retry with
exponential backoff, soft-failing to the seed so broadening never breaks
prediction) and EpcComparablePropertiesRepository.candidates_near. Wired into
the handler and e2e runner; broadening is lazy (only on gate-out) and memoised
per (postcode, property_type).
Validated live: property 728476 (gas boiler) prices system_tune_up at GBP295;
property 718580 (lone flat in BR6 6BS) now predicts via nearby BR6 postcodes.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Corpus validation of the modelling_e2e photovoltaic_supply-as-list fix. Cert
6102-6227-8000-0083-2292 (RdSAP-20.0.0 semi, gas combi + 2× 1.14 kW PV arrays)
crashed from_rdsap_schema_20_0_0 on the measured-array list; the fix routes it
through the dict-tolerant _map_schema_21_pv. PV correctly credited: engine 61
(no PV) → 66 (+5). Built in Elmhurst (evidence: epc.json + summary + worksheet,
fabric+heating; the PV "New Technologies" Panel-details grid deferred): worksheet
55 = engine-on-Elmhurst-inputs 55 exactly → calculator faithful. The +6 engine-vs-
Elmhurst base-dwelling residual is the documented RdSAP-default gap (band-C cavity-
uninsulated suspended-floor semi). Pinned engine 66.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
tests/orchestration/test_postcode_splitter_orchestrator.py imports
`from moto import mock_aws` (moto 5.x) but moto was absent, so the file
errored at collection. Pin moto[s3,sqs]==5.0.28 (S3+SQS are the only mocked
services); resolves cleanly against the boto3 1.35.44 and cryptography 43.0.3
pins. All 4 tests pass.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
@task_handler never built or passed cloud_logs_url, so every app using
it (incl. modelling_e2e) ran run_subtask with the None default and the
CloudWatch deep-link was never saved onto the SubTask. @subtask_handler
did this correctly.
Extract the URL builder into a shared utilities/aws_lambda/cloud_logs.py
(public cloudwatch_url()), use it from both handlers, and pass the URL
into run_subtask from @task_handler. Add regression tests.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
A Landlord Override can reference a building part the lodged or predicted EPC
never carried (e.g. an extension_1 override on a property whose EPC has only
main). apply_simulations indexed parts_by_id[identifier] unguarded, raising
KeyError and aborting the whole property's modelling. Now the orphaned part is
skipped. Recovers 14 of the 22 modelling_e2e failures in portfolio 796.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
@task_handler never built or passed cloud_logs_url, so every app using
it (incl. modelling_e2e) ran run_subtask with the None default and the
CloudWatch deep-link was never saved onto the SubTask. @subtask_handler
did this correctly.
Extract the URL builder into a shared utilities/aws_lambda/cloud_logs.py
(public cloudwatch_url()), use it from both handlers, and pass the URL
into run_subtask from @task_handler. Add regression tests.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
