8e33a8de53
A FLAT roof lodges its insulation thickness in the DEDICATED gov-EPC API `flat_roof_insulation_thickness` field (e.g. "75mm"), leaving `roof_insulation_thickness` None (that field is for pitched-loft joists). `heat_transmission_from_cert` read only the latter, so a measured flat-roof thickness was ignored and the roof billed at the uninsulated age-band flat default (age E = 1.5) instead of its Table-16 insulated U. Fixed by preferring `flat_roof_insulation_thickness` when the part is a flat roof — the exact mirror of the existing rafter-thickness branch. An "AB"/"NI" (as-built/unknown) value parses to None and keeps the age-band default, unchanged; only measured thicknesses move. PER-CERT ELMHURST VALIDATION (cert 47084930, top-floor flat, flat roof 75 mm): built on the lodged inputs in accredited Elmhurst RdSAP10-Online (evidence saved: elmhurst_summary.pdf / elmhurst_worksheet.pdf). The worksheet bills "insulated flat roof" at U 0.5 (floor 0.70 + wall 0.25 also matching the engine). The fix takes the engine roof 96.4 -> 32.1 W/K (= 64.26 x 0.5, Elmhurst-exact), PE +47 -> +0.2, SAP 69.51 -> 74.34 = lodged 74. Gauge: within 77.3% -> 77.7%, SAP MAE 0.648 -> 0.641, CO2 0.074 -> 0.072, PE 3.1 -> 2.97. Unit-pinned in test_heat_transmission (flat_roof_insulation_thickness -> U 0.5); RealCertExpectation 47084930 = 74 (Elmhurst-validated). Also adds the build script build_47084930.py. 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