7281b7b300
The API schema lodges `glazing_type` (int code) per window but `window_transmission_details=None` and `frame_factor=None`. Without per-window U lodgement the cascade falls back to a single global `u_window(None,None,None)=2.5` × total area, which over-shot cert 001479's window W/K by +2.63 (cascade 46.23 vs worksheet 43.60). Fix: `_API_GLAZING_TYPE_TO_TRANSMISSION` lookup translates `glazing_type` → (u_value, solar_transmittance, frame_factor) and the mapper populates `WindowTransmissionDetails` + `frame_factor` per window so the cascade uses its per-window U fast path (each window contributes A × U_eff_individual rather than total_area × U_eff_global). Two codes mapped now: 3 → DG pre-2002 U=2.8 g=0.76 FF=0.70 13 → DG post-2022 Argon U=1.4 g=0.72 FF=0.70 Cert 001479 lodges 8 Main windows at glazing_type=3 + 1 Ext1 window at glazing_type=13 — exactly the manufacturer-lodged worksheet values. The cascade now matches the worksheet's `Windows 1: 13.96 × 2.518 = 35.15 W/K` and `Windows 2: 6.37 × 1.3258 = 8.45 W/K` → **windows W/K EXACT 43.5962**. **Cert 001479 API path: fabric heat loss is now COMPLETELY EXACT across all 6 components** (walls/party/roof/floor/windows/doors all match worksheet at the worksheet's 4 d.p. precision). Total fabric: 139.4957 W/K ✓ (was 122.6130 before Slice 87) walls: 39.7652 ✓ party walls: 17.0700 ✓ roof: 10.3438 ✓ floor: 23.1705 ✓ windows: 43.5962 ✓ doors: 5.5500 ✓ API SAP delta progression through Slices 87-93: Slice 87 baseline: +3.0752 After Slice 90: +1.5298 (party walls) After Slice 91: +1.0970 (descriptive strings + roof desc) After Slice 92: +1.0022 (floor dims) After Slice 93: +1.1846 (windows — fabric now EXACT) The +1.18 SAP gap is now PURELY non-fabric: candidates are internal gains, solar gains, ventilation, MIT, or hot water cascade — to diagnose in the next slice. Golden cert residuals updated for the cascade improvements. Pyright net-zero on mapper.py (33 → 33). 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