6be8fdb7b6
SAP 10.2 §3.2 applies the 0.04 m²K/W curtain resistance per window;
the worksheet's (27) column shows it that way. Our calc had been
applying it ONCE to the area-weighted-avg raw U across all windows.
That's correct when all windows share a U but biased when a dwelling
has mixed glazing types (typical Elmhurst fixture lodges 2 types):
U_eff(weighted_avg(U_i)) ≠ weighted_avg(U_eff(U_i))
because 1/(1/U + 0.04) is non-linear. The drift was ~0.05-0.10 W/K
on `windows_w_per_k` for 000474, 000477, 000487 (mixed-glazing
fixtures).
Fix: when sap_windows have per-window u_value lodged (the spec-
faithful path), iterate them computing per-window U_eff × area and
sum. Falls back to the legacy single-avg-U path when window U isn't
lodged (back-compat for synthetic tests that pass
`window_avg_u_value=...` directly).
Per-window LINE_27 numbers now match PDF exactly:
fixture | windows W/K calc → PDF | LINE_33 Δ before → after
--------|------------------------|---------------------------
000474 | 25.4243 → 25.3674 ✓ | +0.0864 → +0.0296 (-66%)
000477 | 17.8550 → 17.8349 ✓ | -0.1045 → -0.1246 (small
widening — exposes
upstream floor-U drift)
000487 | (cascading) | +37.88 (RR defect, slice 23)
000480 | unchanged | -0.0168 → -0.0168 (single U)
000490 | unchanged | +0.0282 → +0.0282 (single U)
000516 | (cascading) | -6.75 (RR defect, slice 23)
Total cascade pin failure count unchanged at 83 (pins still above
abs=1e-4 floor by 0.03-0.13 W/K — sub-display-precision drift left
in floor-U cascades + the two RR fixture defects).
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