31c01a7e8c
`heat_transmission_from_cert` computed `y = thermal_bridging_y(age_ band=part.construction_age_band)` per bp, then applied each bp's y to its own external area. That mis-models multi-age dwellings: RdSAP10 Table 21 indexes y by the *dwelling's* age band, and Elmhurst's worksheet reports y as a single user-defined value applied to total exposed area (cert 001479 worksheet: "Thermal Bridges Bridging User Input Y 0.15"). For cohort certs with uniform age-band bps the change is heat-loss- invariant. For cert 001479 (Main=C → 0.15, Ext1=M → 0.08, Ext2=C → 0.15) the cascade was under-counting Ext1's bridging by 0.07 × 27.28 m² ≈ 1.9 W/K. For golden cert 7536-3827 (Main=D, Ext1=L, Ext2=F) the same per-bp split was costing ~2 W/K of bridging. Use the primary part's (parts[0]) age band for a single dwelling-wide `dwelling_y`, applied across all parts in the heat-loss loop. Cert 001479 chain pin closes another step: cascade SAP 70.38 → 70.20 (target 69.0094, delta 1.37 → 1.19). Golden 7536-3827 residuals tighten in lockstep: SAP +4 → +3, PE -24.73 → -22.53, CO2 -0.66 → -0.60. Other 7 golden certs unchanged (single-bp or uniform-age multi-bp). 70 of 71 chain+golden+heat-transmission tests green; chain pin still RED (load-bearing). Pyright net-zero (13-error baseline on heat_transmission.py preserved). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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|---|---|---|
| .devcontainer | ||
| .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