9830ea2110
The Elmhurst Summary lodges only the secondary heating SAP code (Table 4a Category 10), never its fuel. `_elmhurst_secondary_fuel_from_sap_code` mapped the gas block (601-613 → mains gas) and solid block (631-634 → house coal) to their modal defaults, but returned None for any OTHER Category-10 code — and None makes the cascade SILENTLY bill the secondary as electricity (13.19 p/kWh). For a fuel-fired heater (e.g. 621-625 liquid-fuel oil/bioethanol) that is a large, invisible mis-price. Per the UnmappedElmhurstLabel strict-raise pattern (mirrors the wall_type / glazing label raises), a fuel-fired Category-10 code (601-699) outside the mapped gas/solid blocks now RAISES instead of guessing. Electric room heaters (691-699) keep returning None — electricity IS their fuel. The gas block 601-613 still resolves to the modal default mains gas: the Summary cannot distinguish mains gas from LPG/biogas, so an LPG or biogas live-effect fire (worksheet "simulated case 37" used biogas at 7.60 p/kWh vs our 3.48 p/kWh mains-gas default, a +7 SAP gap) is not recoverable from the Summary export — that is a data-availability limit, not a guess we can fix here. This commit closes the genuinely-silent-wrong path; the gas sub-fuel remains the documented modal default. Worksheet harness 47/47, 0 raised. 3 AAA tests, pyright net-zero, regression clean, corpus gauge unchanged (Elmhurst-path only; the API path lodges the secondary fuel explicitly). 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 | ||
| harness | ||
| 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 | ||
| next_claude_prompt.txt | ||
| 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