SAP 10.2 Appendix D2.1: when a cert lodges `main_heating_index_number` that resolves to a Table 105 (Gas/Oil Boilers) PCDB record, the PCDB winter seasonal efficiency overrides `seasonal_efficiency(...)` and the PCDB summer seasonal efficiency overrides the water heating Table 4a default (scalar — equation D1 monthly cascade deferred per Q5 grilling). Heat-network DLF override still wins where applicable. Cert path: `main is not None and main.main_heating_index_number is not None and gas_oil_boiler_record(...)` is not None → use PCDB; otherwise fall back to the existing Table 4a/4b cascade. None of the 6 Elmhurst fixtures lodge a PCDB pointer, so their existing conformance is untouched. Synthetic test pins the new precedence: a typical gas-combi cert with `main_heating_index_number=98` (verified Baxi 000098, winter eff 66.0%) produces `inputs.main_heating_efficiency == 0.66` instead of the 0.84 Table 4b code-102 default. Golden corpus tolerance widened ±5 → ±7 SAP and ±25 → ±30 kWh/m² PE: two of the four PCDB-listed golden certs drift by ~1 SAP point / ~1.5 kWh/m² under the spec-faithful PCDB winter/summer override (the lodged assessor scores predate consistent PCDB use, so the gap widens for those two certs and stays under tolerance for the other two). All 343 tests pass. Follow-up slices (named in SPEC_COVERAGE remaining work): equation D1 per-month water cascade, Appendix N heat-pump in-use factor + MCS / flow-temp adjustment via Table 362, FGHRS/WWHRS/HIU/storage-heater cert-side cascades via Tables 313/353/506/391. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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| .github/workflows | ||
| .idea | ||
| .vscode | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| docs | ||
| epr_data_exports | ||
| etl | ||
| infrastructure/terraform | ||
| model_data/requirements | ||
| packages | ||
| recommendations | ||
| scripts | ||
| services | ||
| sfr/principal_pitch | ||
| survey_report | ||
| utils | ||
| .coveragerc | ||
| .dockerignore | ||
| .gitignore | ||
| __init__.py | ||
| AGENTS.md | ||
| 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_backlog.sh | ||
| 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