6b04514645
A Summary §14.0 Table 4b gas boiler (SAP code 101-119) lodges no §14.0 "Fuel Type" string in the newer Elmhurst export. The carrier was resolved only from §15.0 "Water Heating Fuel Type" — fine when the same boiler heats the water, but a gas boiler paired with a SEPARATE electric immersion lodges §15.0 "Electricity", so `_elmhurst_gas_boiler_main_fuel` returned None and the cascade strict-raised MissingMainFuelType. Cert 001431 boiler-1/boiler-2 "before" variants are exactly this config: §14.0 SAP code 102/104 (mains-gas boiler), §15.0 electric immersion (code 909), §14.2 Meters "Main gas: Yes". The meter flag is the authoritative carrier signal — a 101-119 boiler on mains gas burns mains gas — so adopt it (SAP10 main_fuel 26 per _ELMHURST_MAIN_FUEL_TO_SAP10 "Mains gas") when §15.0 can't disambiguate. §15.0 gas/LPG still wins when present (keeps LPG-vs-mains-gas precision); no mains-gas meter + non-gas §15.0 still strict-raises rather than guessing. Spec: SAP 10.2 Table 4b "Seasonal efficiency for gas and liquid fuel boilers" (PDF p.168), rows 101-119. Both certs now resolve main_fuel=26 and compute (was: hard raise). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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| .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