963db2ae23
Simulated case 6 (P960-0001-001431, dual oil boiler 51% rads + 49% underfloor) worksheet (231) = 356 = (230c) central-heating pump 156 + (230d) oil boiler pump 200. (230c) decomposes per SAP 10.2 Table 4f note c) (PDF p.175): "Where there are two main heating systems include two figures from this table" — Main 1 41 kWh (pump age "2013 or later") + Main 2 115 kWh (pump age unknown). The cascade summed only Main 1's circulation pump, giving (231) = 241. cert_to_inputs now adds the second main's circulation pump, gated on a lodged main_heating_fraction > 0 (a genuine second SPACE-heating main — the same test §9a uses to split space-heating demand). This excludes DHW-only second mains (cert 000565 Main 2 = gas combi via WHC 914, fraction 0); without the gate 000565's worksheet pins regressed +115 kWh. Re-pin: golden 0240 (dual-main oil combi, API-only, no worksheet) gains its Main 2 pump too (pumps_fans 315 → 430). Spec-correct per note c and validated by the case-6 worksheet; SAP cont 72.55 → 72.18 (integer 73 → 72, resid +0 → -1), PE +1.9459 → +2.8092, CO2 +0.1226 → +0.1385. The lodged 73 carries Elmhurst's own residual; the worksheet- backed case 6 is the spec authority for the archetype. Note: the boiler-interlock −5pp per-main determination the prior handover flagged as the priority is already implemented (S0380.141 cylinder-thermostat path + S0380.177 room-thermostat path) — case 6 already produces (206)=79 / (207)=84 exactly, and 0240 is a combi with no cylinder so correctly unpenalised. 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