The Elmhurst Summary §3.0 "Date Built" lodges the per-building-part age
bands; the Main row reads "Main Property" / "C 1930-1949". But "Main
Property" ALSO heads the §4.0 Dimensions table, so the global
`_str_val("Main Property")` collides with it: when pdftotext renders
"3.0 Date Built:" glued onto its "Main Property" row token on one
layout line (as the recommendation worksheets do), the first standalone
"Main Property" match is the §4 dimensions header — returning its next
token "Floor" as the "age band".
That garbage age propagated to `u_roof`: for a "Pitched, sloping
ceiling" (PS) roof with no lodged insulation thickness, `u_roof` returns
the spec uninsulated U=2.3 for the correct age C but U=0.4 for the
unparseable "Floor" — collapsing the roof heat-loss term and inflating
SAP by ~14 points on the affected cert.
Scope the read to the Date-Built block (between "3.0 Date Built" and
"4.0 Dimensions") and take the first age row — a line beginning with a
single A-M band letter + space ("C 1930-1949", "A before 1900",
"J 2003-2006"). Building-part name rows never start that way, and the
Main row precedes any extension / room-in-roof rows.
Regression: full sap10_calculator + documents_parser suite green bar the
3 pre-existing unrelated fails (2 stone-wall U tests, test_total_floor_
area); the multi-bp / "A before 1900" fixtures (000516, 001431_case*,
6035) keep their age bands.
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 | ||
| 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