f869a1f6a7
Fitting sealed glazing units changes two things beyond the pane's U/g
that the cascade reads, which the overlay didn't model — leaving the
double/secondary before→after pins ~0.7 SAP short (xfail):
1. Draught-proofing (RdSAP 10 §8.1). Sealed units draught-proof the panes
they replace, re-lodging the dwelling-level `percent_draughtproofed`
(cert 001431: 84 → 100). The §2 cascade reads that dwelling-level
value, so the overlay now carries it. `_recompute_percent_draughtproofed`
anchors on the lodged before-% — `after = round((round(before%/100 × N)
+ flips) / N × 100)`, N = openable windows (vertical + roof) + doors,
flips = upgraded panes that were not draught-proofed — so it's robust
to incomplete window extraction (unchanged openings are already in the
aggregate). ~0.3 SAP.
2. Frame factor (§6 solar gains). A replacement unit re-lodges its own
FF=0.70, overriding the pane it replaced — the two "single glazing,
known data" panes lodge FF 1.00 / 0.50 (one is 6.6 m²), so leaving them
unchanged understated solar gains by ~+150 kWh space heating. `WindowOverlay`
now carries `frame_factor`, written flat onto the window. ~0.4 SAP.
Wiring: `EpcSimulation.percent_draughtproofed` + `WindowOverlay.frame_factor`
new fields; `apply_simulations` / `_fold_window` write them; the glazing
generator computes both from the upgraded set and cert 001431's after.
Un-xfails `test_{double,secondary}_glazing_overlay_reproduces_the_relodged_after`
— both now pin SAP/CO2/PE to the relodged after within tolerance. Updates
the two `test_glazing_recommendation` overlay expectations for the new
`frame_factor`. 96 modelling tests pass; zero new pyright errors.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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| .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 | ||
| package-lock.json | ||
| package.json | ||
| pyproject.toml | ||
| pyrightconfig.json | ||
| pytest.ini | ||
| README.md | ||
| 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