bd25a3c774
RdSAP10 `wall_construction == 6` is canonically WALL_SYSTEM_BUILT — a WALL TYPE — but the gov-EPC basement heuristic hijacked it: Elmhurst lodges both "SY System build" and "B Basement wall" as code 6, and the API lodges basements as code 6 too, so a system-built wall was mis-flagged `main_wall_is_basement` → wrong RdSAP §5.17 / Table 23 u_basement_wall/u_basement_floor overrides, and downstream the solid-wall Recommendation Generator couldn't offer EWI/IWI on system-built walls. System-built stays the wall type on its canonical code 6; the basement signal moves OFF code 6 to a dedicated `is_basement` (SapAlternativeWall) / `wall_is_basement` (SapBuildingPart) Optional[bool] flag: - Elmhurst: `_elmhurst_wall_is_basement` sets it from the distinct "SY"/"B" labels (False for SY, True for B, None otherwise). - gov-EPC API: per-wall code 6 can't be told apart at lodging time, so `from_api_response` post-processes via `_clear_basement_flag_when_ system_built` — when the cert addendum marks the dwelling system-built, the code-6 basement heuristic is cleared. A genuine basement (no addendum signal) keeps the code-6 fallback. - `main_wall_is_basement` / `is_basement_wall` honour the flag when set, else fall back to the code-6 heuristic — so untouched API basements and the cert 000565 "B" cohort are unchanged. `EpcPropertyData.system_build` is a derived property over the wall type: the MAIN wall is system-built iff `wall_construction == 6` and it is not flagged basement. System-built lives on `wall_construction`; the basement attribute is separate. Acceptance: a system-built main wall (Elmhurst SY, or API addendum system_build) → wall_construction == 6, main_wall_is_basement is False, system_build is True; a genuine basement main wall → main_wall_is_basement is True, system_build is False. Full §4 suite green (2404 passed). 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