e1348c424b
For flats, `EpcPropertyData.dwelling_type` needs a "Top-floor" /
"Mid-floor" / "Ground-floor" prefix so the cascade's
`_dwelling_exposure` (cert_to_inputs.py) gates floor + roof party-
surface routing correctly per RdSAP 10 §5. Before Slice 99a, the
broken `built_form` ("2.0 Number of Storeys:") meant cert 9501's
`dwelling_type` was "2.0 Number of Storeys: flat" — never matched
any flat-prefix in the cascade, so the cert was treated as a fully-
exposed dwelling (worksheet had floor U=0 / party-ceiling-down, but
cascade routed both as exposed → Δ +9.25 W/K on floor alone). After
99a's empty-attachment fix the prefix was just " flat" — still no
match.
Slice 99b composes the position prefix from the Summary's lodged
floor location + RR presence:
- floor.location lodges "dwelling below" → floor is party
- + RR present → Top-floor (roof exposed)
- + no RR → Mid-floor (roof party)
- floor.location doesn't lodge dwelling below → Ground-floor
For cert 9501: floor.location="A Another dwelling below" + RR
present (cert lodges Room-in-Roof with gable walls + flat ceiling).
Resulting `dwelling_type` = "Top-floor flat" — matches the cascade's
`_dwelling_exposure` "top-floor" prefix → has_exposed_floor=False,
has_exposed_roof=True, the worksheet's exposure shape.
Houses keep the historical contract: `f"{built_form}
{property_type.lower()}"` — cohort hand-builts and the 2 boiler
chain tests (001479 + 0330) unchanged.
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