10437143c4
Cert 000565 surfaced a per-extension Room(s) in Roof coverage gap.
§4 Dimensions lodges an RR floor area for every BP (Main + each
extension) and §8.1 lodges full construction details per BP. The
old extractor parsed RR from §4 + §8.1 for Main only — the 4
extensions' RR areas (34 + 5 + 32 + 2 = 73 m²) were silently
dropped, leaving TFA at 246.91 m² vs the worksheet's 319.91 m²
(23% deficit).
Schema:
- `ExtensionPart.room_in_roof: Optional[RoomInRoof] = None` field.
None for single-storey extensions (no RR lodged); populated for
every extension that lodges a §4 RR floor area > 0.
Extractor:
- `_room_in_roof_from_bodies(dim_body, rir_body, age_band)`
parameterises the previously Main-only `_extract_room_in_roof`
so the same parsing applies to each extension.
- `_extract_extensions` now slices §8.1 by BP (alongside the
existing §4/§7/§8/§9 slicing) and reads each extension's RR age
band from §3's "<N>th Ext. Room(s) in Roof <band>" line via a
new regex.
- A new defensive "§4 lodges RR area but §8.1 has no construction
details" branch returns a partial `RoomInRoof` with empty surfaces
so the cascade still attributes the floor area to TFA. (Not
triggered on 000565 — all 5 BPs lodge construction details — but
needed for older Elmhurst variants per the existing extractor
comment style.)
Mapper:
- `_map_elmhurst_building_parts` now passes each extension's
`room_in_roof` through `_map_elmhurst_room_in_roof` to the
extension's `SapBuildingPart.sap_room_in_roof`. Previously the
loop hardcoded the field as None.
- `total_floor_area_m2` derivation now also sums each extension's
`room_in_roof.floor_area_m2`. Without this, the per-BP RR floor
area is lodged on the BP but the cert's top-level TFA stays at
the pre-fix value.
Cert 000565 cascade impact:
- TFA: 246.91 → 319.91 ✓ (matches U985-0001-000565.pdf Block 1)
- space_heating_kwh_per_yr: Δ −9,107.71 → −1,099.50 (88% reduction)
- main_heating_fuel_kwh_per_yr: Δ −5,357.47 → −646.76 (88% reduction;
space_heating × 1/HP COP — main_heating tracks space_heating)
- lighting_kwh_per_yr: Δ −236.19 → +2.18 (essentially closed —
RdSAP §12-1 lighting is TFA-proportional)
- hot_water_kwh_per_yr: Δ +214.50 → +271.84
- co2_kg_per_yr: Δ −1,438.16 → −751.06
- total_fuel_cost_gbp: Δ −1,055.62 → −564.05
- sap_score_continuous: Δ +1.70 → +6.75 (cost/TFA dropped because
cost rose ~14% but TFA rose ~30% — the remaining −564 cost gap
has to close before SAP catches up)
Single-storey-extension certs: `room_in_roof=None` for each extension
(no §4 RR lodgement), no behavioural change. Cohort regression check:
415 pass + 10 expected 000565 fails — no regression on the 14 Summary
fixtures + JSON fixtures that don't carry per-extension RR.
Pyright net-zero on all 3 touched files (32 / 0 / 0).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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|---|---|---|
| .devcontainer | ||
| .github/workflows | ||
| .idea | ||
| .vscode | ||
| applications | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| deployment/terraform | ||
| docs/adr | ||
| domain | ||
| epr_data_exports | ||
| etl | ||
| infrastructure | ||
| model_data/requirements | ||
| orchestration | ||
| recommendations | ||
| repositories | ||
| scripts | ||
| sfr/principal_pitch | ||
| survey_report | ||
| tests | ||
| utilities | ||
| utils | ||
| .coveragerc | ||
| .dockerignore | ||
| .gitignore | ||
| __init__.py | ||
| ara_backend_design.md | ||
| BaseUtility.py | ||
| CLAUDE.md | ||
| conftest.py | ||
| CONTEXT.md | ||
| devcontainer.sh | ||
| Dockerfile.test | ||
| Dockerfile.test.dockerignore | ||
| Makefile | ||
| MEMORY.md | ||
| package-lock.json | ||
| package.json | ||
| pyproject.toml | ||
| pyrightconfig.json | ||
| pytest.ini | ||
| README.md | ||
| run_lambda_local.sh | ||
| serverless.yml | ||
| test.requirements.txt | ||
| tox.ini | ||
| UBIQUITOUS_LANGUAGE.md | ||
Model Repository
This repository contains the code pertaining to the development of the data science and machine learning products being utilised by Hestia.
The different folders in this repository relate to services that can be used independently, or can be imported and used as part of a larger application
Getting Started
Prerequisites
Dev Container Setup
This repo uses a Docker Compose-based dev container. The model-backend service joins a shared-dev Docker network so it can communicate with other local services (e.g. a frontend container) running on your machine.
VS Code users: The initializeCommand in devcontainer.json creates the shared-dev network automatically before the container starts. No manual step required — just open the repo and select Reopen in Container.
Non-VS Code / CI workflows: Run the following once before starting the container:
make dev-setup
This is idempotent and safe to re-run if the network already exists.
Folders
backend/
This folder contains the code for the fastapi backend service, which provides an interface to much of the functionality in this repository, for the frontend
model_data/
This folder contains related to the reading and preparation of assessment model data, including pulling out epc attributes
Testing
All tests can be run, against the configuration in pytest.ini running
pytest
This will run the complete panel of tests and report on coverage in the locations specified by the pytest.ini file.
To run tests in a specific service, e.g. inside of model_data, simply run
pytest --cov-config=model_data/.coveragerc --cov=model_data
This will produce the test results and coverage reports