5cae4a0ce5
_u_brick_thin_wall_age_a_to_e's `<= 280` check put an uninsulated solid- brick wall of exactly 280mm in the "200 to 280mm -> 1.7" row; RdSAP 10 §5.7 Table 13 (spec PDF p.41) shares 280 as an unlabelled edge between that row and "280 to 420mm -> 1.4", so the table text alone doesn't say which row owns it. Found by building cert 100031768368 (280mm exactly, band C) in Elmhurst and comparing every worksheet line to the calculator: volume/ACH/floor/ doors matched exactly, but the wall didn't (1.70 vs Elmhurst's 1.40) despite an identical input crosswalk. Initially reverted a first attempt at this fix when it appeared to regress an already-pinned cert (217091901, band A, also nominally 280mm, previously "confirmed" at U=1.70) — but that cert's build script never actually set a wall thickness field, so its shared Elmhurst assessment had silently inherited a stale 260mm from an earlier build. Fixed that build script and rebuilt with the correct 280mm entry: Elmhurst's worksheet now also gives U=1.40, matching 100031768368 and confirming the fix rather than contradicting it. Both certs move to (near-)exact lodged matches: 100031768368 59.12 -> 61.21 (lodged 61, within 0.5); 217091901 60.82 -> 61.59 (lodged 62, exact). Corpus gauge 77.8% -> 78.6% within-0.5, MAE 0.636 -> 0.627 (14 corpus certs lodge exactly 280mm solid brick). TDD'd, 46 pins + full suite green (2310 passed). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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| .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