0e9bceb2d4
A pre-SAP10 RdSAP cert that omits per-window records lodges glazing as a
`multiple_glazing_type` code plus a `window.description` and a
`multiple_glazed_proportion`. When the numeric code is the "ND" (Not
Defined) sentinel, the reduced-field synthesis defaulted to the DG-modal
code 2 (double) — ignoring the two register signals that say otherwise.
`_normalize_sap_schema_16_x` already honours an explicit "Single glazed"
description for 16.x certs (rewriting multiple_glazing_type -> 5), but that
rule was never propagated to the 17.0 / 17.1 / 18.0 / 19.0 / 20.0.0
synthesis seams — they all blindly applied ND -> 2. So a genuinely
single-glazed dwelling was:
1. scored as double-glazed, overstating its baseline SAP, and
2. hidden from the glazing generator (which upgrades only single-glazed
windows), producing no glazing recommendation.
Reproduced on cert 2780-3922-3202-6042-9200 (uprn 10033526327), a
single-glazed community-heated flat lodging multiple_glazing_type="ND",
window "Single glazed", multiple_glazed_proportion 0 — mapped to four
double-glazed windows, no glazing measure offered.
Extend the 16.x rule to all five reduced-field seams via one shared
resolver: on the ND fallback, when the description says "single" or the
multiple-glazed proportion is 0, synthesise single glazing
(`_api_cascade_glazing_type(5) == 1`, SAP10 cascade single, Table 24
U~4.8) — the exact single code the glazing generator's {1, 15} set
matches — else keep each seam's DG-modal default. Refines ADR-0028's ND
handling.
Tests: all five seams synthesise single from a "Single glazed" + 0%
proportion cert; the double-glazed control keeps the DG-modal default;
plus unit pins on the resolver.
Co-Authored-By: Claude Opus 4.8 (1M context) <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