c75ef6417f
Golden cert 0390-2954-3640 (detached, TFA 360, age F) carried a +7 SAP / -28 kWh/m² PE residual the audit attributed to a demand-side fabric gap. Walking the §3 cascade localised it to the Main wall: lodged wall_construction=4 (cavity), wall_insulation_type=4 (as-built / assumed), description "Cavity wall, as built, partial insulation (assumed)". The cascade mis-routed it to the Table 6 "Filled cavity" row (band F = 0.40) because `_described_as_insulated` matches the "partial insulation" substring. RdSAP 10 Specification (10-06-2025) Table 6 — Wall U-values, England distinguishes two cavity rows: "Cavity as built" A-E 1.5, F 1.0, G 0.60, H 0.60, I 0.45, J 0.35, ... "Filled cavity" A-E 0.7, F 0.40, G 0.35, H 0.35, I 0.45†, J 0.35†, ... An "as built ... partial insulation (assumed)" cavity is the as-built partial fill of the age band, NOT a retrofit cavity fill (a genuine fill lodges the distinct "Cavity wall, filled cavity", wall_insulation_type=2). It therefore routes to "Cavity as built" (band F = 1.0), mirroring the worksheet-validated solid-brick rule in S0380.209 (cases 9/10: "as built, insulated (assumed)" → as-built age-band row, not retrofit). New `_cavity_described_as_filled` predicate is used only in u_wall's cavity filled-row branch; it excludes the "partial insulation" substring while keeping "insulated (assumed)" → filled (the unrelated, separately asserted test_cavity_as_built_insulated_assumed_uses_filled_cavity_row is unchanged). The shared `_described_as_insulated` (also consumed by the roof/floor paths) is left untouched. Wall HLC +53.6 W/K (U 0.40 → 1.0 over ~268 m²) lifts all four metrics together — the signature of a real fabric bug, not a tuned offset: SAP +7 → +0 PE -27.9745 → +0.5281 kWh/m² CO2 -2.7134 → -0.1189 t/yr Bands I-M are unaffected (the two rows coincide per the † footnote), so golden certs 0535 (band M) / 7536 (band L) with "insulated (assumed)" cavities continue to pin at 0. Full suite 2384 passed, 1 skipped. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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| .devcontainer | ||
| .github/workflows | ||
| .idea | ||
| .vscode | ||
| applications | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| deployment/terraform | ||
| docs | ||
| domain | ||
| epr_data_exports | ||
| etl | ||
| infrastructure | ||
| model_data/requirements | ||
| orchestration | ||
| recommendations | ||
| repositories | ||
| sap worksheets | ||
| 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