03c4ea4921
Cert 2636-0525-2600-0401-2296's Summary §11 Windows block lodges one
alt-wall window (1.19 m², north-facing). The PDF layout for alt-wall
rows puts the "Alternative wall" string in the slot BEFORE the W×H×A
data line — not after frame_factor where regular "External wall"
rows put it. Without this fix the extractor's
`_parse_window_from_anchors` only scanned the post-frame_factor
`middle` slice for wall tokens, defaulted to "External wall" for the
alt-wall row, and the cascade allocated the 1.19 m² opening to the
main wall instead of the alt-wall — under-deducting from main and
leaving the alt-wall gross instead of net.
Fix at `elmhurst_extractor.py:865`: also scan
`lines[before_start:data_idx]` (the pre-data slice) for "wall"
tokens. Search order:
1. `middle` — first preference (normal layout for regular rows)
2. `pre_data` — alt-wall rows (cert 2636)
3. "External wall" default — no wall lodging found
Forcing function: cert 2636 walls_w_per_k moves from 20.5595 to
**20.0240 — EXACT match against worksheet (29a) Main 11.9250 + alt.1
8.0990 = 20.0240**. (Header (29a) sum is now fabric-exact; the
remaining +0.52 SAP residual on cert 2636 is in the ventilation
cascade — HTC 153.97 vs API 159.02 vs worksheet (39) avg 158.85 —
to be investigated in a follow-up slice.)
Added focused unit test
`test_summary_2636_alt_wall_window_parses_alternative_wall_location`
that pins the by-area lookup: 1.19 m² → "Alternative wall"; the
six 2.25 m² windows stay on "External wall". Guards against future
window-location parser regressions.
Pyright: 0 errors on the edited extractor + test files.
Regression suite: 685 pass + 10 fail (handover baseline 669 + 10 +
16 new GREEN tests across S0380.2..S0380.12). Cohort status:
cert Δ vs worksheet spec floor?
0380 +0.0594 ✓
0350 +0.0458 ✓
2225 +0.0441 ✓
2636 +0.5167 ✗ (fabric exact; ventilation residual)
3800 +0.0442 ✓
9285 +0.0502 ✓
9418 +2.5973 ✗ (Daikin)
Spec refs:
- Slice 102f-prep.10 (commit
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| .devcontainer | ||
| .github/workflows | ||
| .idea | ||
| .vscode | ||
| applications | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| deployment/terraform | ||
| docs/adr | ||
| domain | ||
| epr_data_exports | ||
| etl | ||
| infrastructure | ||
| model_data/requirements | ||
| orchestration | ||
| recommendations | ||
| repositories | ||
| 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