51d8f65aac
interpolate_heat_pump_efficiency_at_psr clamped to the smallest/largest PSR row when the dwelling's plant size ratio fell outside the record's range. That is the SAP 10.2 Appendix N rule for *combined heat-pump-and-boiler* packages, not for a plain air/ground/water source heat pump. Per Appendix N2 (PDF p.101, footnotes 44/45) a source heat pump whose PSR exceeds the record's largest value takes a reciprocal-linear interpolation between the largest-PSR efficiency and 100% at twice that PSR (100% beyond), and 100% when the PSR is below the record's smallest value. Both the space- and water-heating PSR-dependent efficiencies extend this way. Effect: an oversized heat pump in a small dwelling is no longer credited the full top-of-table COP. Accredited Elmhurst worksheet for cert 100110101713 (golden fixture case 56, PCDB 100061, PSR 3.107 over largest 2.0): (206) 334.4% -> 139.66% = Elmhurst exact. Corpus (RdSAP-21.0.1, n=1000) MAE 0.7397 -> 0.7258, within-0.5 0.7410 held; only two certs move (both oversized-PSR heat pumps), 100110101713 +18.32 -> -4.97. Exhaust-air and combined heat-pump-and-boiler packages have different boundary rules (straight-to-100% / clamp-to-edge) but are not distinguished by the current PCDB parse; the air/ground/water rule is applied uniformly, a documented limitation noted in the function docstring. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
||
|---|---|---|
| .claude/skills | ||
| .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 | ||
| modelling_audit.md | ||
| next_claude_prompt.txt | ||
| P960-0001-001431-2.pdf | ||
| package-lock.json | ||
| package.json | ||
| playground.py.local-backup | ||
| pyproject.toml | ||
| pyrightconfig.json | ||
| pytest.ini | ||
| README.md | ||
| run_lambda_local.sh | ||
| serverless.yml | ||
| Summary_001431-3.pdf | ||
| 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