9cb98344fa
A Simplified room-in-roof (RdSAP 10 §3.9.1, PDF p.21) does NOT measure
its slope / flat-ceiling / stud-wall surfaces — the Elmhurst Summary
lodges placeholder Length/Height cells (a 40 m flat-ceiling height, a
32 m slope on a 4.65 m-wide gable). The spec instead derives one
timber-framed "remaining area" from the floor area:
A_RR = 12.5 × √(A_RR_floor / 1.5) §3.9.1(d)
A_RR_final = A_RR − ΣA_RR_gable/other §3.9.1(e)
The cascade already computes A_RR_final itself (heat_transmission.py:
`12.5 × √(A_RR_floor/1.5) − rr_walls_in_a_rr_area` residual), but only
when `detailed_surfaces` carries no roof-going kind (`has_roof_lodgement`
gate). `_map_elmhurst_rir_surface` emitted the placeholder slope/ceiling
rows as raw L×H for every assessment type, flipping that gate and billing
1024 m² + 160 m² of explicit roof area — a 7.5× fabric-heat-loss
explosion (cert 001431 sim case 2: SAP −14.6 vs worksheet 69, space
heating 114 378 vs ~15 000 kWh).
Fix: for a Simplified assessment, drop the roof-going surfaces in the
mapper so the cascade's residual formula fires. This matches how the API
path already (correctly) handles the same Simplified RR — scalar gable
fields, no roof-going detailed_surfaces (golden cert 6035) — and the
gables-only cert 000565. Detailed (§3.10) assessments still measure these
surfaces and keep them.
With the fix, sim case 2 total external area = 232.94 (worksheet exact),
roof 78.33 (was 2725.89), SAP 69.29 → worksheet integer 69. A small
residual (~450 kWh main fuel) remains — a separate fabric gap to walk
next. 2308 passed (+2), 0 failed; pyright net-zero.
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