00a27efd87
The §11 Windows table in the Summary PDF doesn't lay out identically across the cohort. Three new quirks added to the layout-style parser so the remaining 5 certs can be debugged with windows actually extracted: 1. `Wood 0.70` combined frame_type+frame_factor line — previously the parser expected them on separate lines (data+1 / data+2) and rejected the window when the joined form appeared. 2. Trailing glazing-type on the data line — `1.22 1.76 2.15 Double pre 2002` is the joined-cell variant in 000516; the W/H/Area anchor now captures the trailing phrase as an optional 4th group and feeds it through as `inline_glazing_type`, bypassing the separate-line glazing-prefix scan. 3. Cross-window gap with no glazing marker — `_partition_after_manuf` now falls back to "second orientation token in gap" when no glazing-type-prefix word appears. Covers the 000516 layout where each window has prefix+suffix orient tokens (no inline orient) and the glazing-type is joined-to-data. The 5 remaining Summary PDFs are copied into `backend/documents_parser/tests/fixtures/` ready for per-cert mapper work. Mirror pin tests deferred — each cert still has its own diff to close (handover in NEXT_AGENT_PROMPT.md documents the per-cert state, e.g. 000477 needs secondary-heating extraction, 000516 needs roof-window separation). Current cohort SAP deltas vs the U985 worksheet PDFs (target 1e-4): 000474 0.0000 ✓ 000477 +6.3655 secondary heating + lighting 000480 +8.2695 diagnosis pending 000487 +8.1433 extractor still drops windows 000490 +5.6551 diagnosis pending 000516 +5.9812 roof-window separation Wider regression stays green (754 pass). Pyright net-zero on touched files. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
||
|---|---|---|
| .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