bb8307413f
A "Pitched, sloping ceiling" (roof_construction == 8) lodges its
insulation in the dedicated `sloping_ceiling_insulation_thickness` field,
not `roof_insulation_thickness` (which stays None — the loft-joist field
is meaningless for a slope-following ceiling). The schema dataclasses
dropped that field, so `from_dict` discarded it and the cascade treated
the slope as uninsulated; worse, the pre-1950 None-fallback forced 0 mm
(U=2.30), over-stating roof heat loss ~74%.
Surface the field on SapBuildingPart (schemas 21.0.0 / 21.0.1) and prefer
it in `_api_resolve_sloping_ceiling_thickness` when it carries a NUMERIC
thickness: "100mm" now reaches Table 17 column (1a) "Insulated slope –
sloping ceiling, mineral wool/EPS" (RdSAP 10 §5.11.3 p.44 — 100 mm →
U=0.40) instead of 2.30. Categorical lodgements ("AB" As Built / "NI")
are not measured thicknesses, so they fall through to the existing
as-built rule (Table 18 col (3) via is_pitched_sloping_ceiling).
Cert 9884-3059-9202-7506 (code 8, age B, sloping 100 mm): SAP −5.54 → +0.06.
Cert 8036-2925-6600-0202: −4.94 → +1.55. No regressions in the roof-8
cohort (the "AB" certs are unchanged). Eval headline 43.8% → 44.3% within
0.5; golden fixtures incl. 6035 green.
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