24a7351fed
RdSAP §1.4.2: window openings deduct from the gross of the wall they pierce. The cert schema lodges `window_wall_type` on each SapWindow: code 1 = main wall, codes 2/3 = alternative walls 1/2. Cohort ground-truth: cert 2636 BP0 lodges one window (1.14 × 1.04 ≈ 1.19 m²) with `window_wall_type=2` → it pierces alt.1 (12.76 m² cavity unfilled at age D → U=0.70). Pre-fix the cascade subtracted ALL openings from the BP's (main+alt) gross then routed each alt at its FULL gross — over-counting alt's contribution by 1.19 × U_alt and under-counting main by 1.19 × U_main. For cert 2636: 1.19 × (0.70 − 0.25) = +0.535 W/K cascade walls excess, matching the observed cascade walls 20.56 vs worksheet 20.024. `_window_on_alt_wall` translates the per-window `window_wall_type` code; the per-BP loop aggregates alt-wall windows into `alt_window_area_by_bp`, passes that opening area through to `_alt_wall_w_per_k` (alt.1 only — no cohort cert exercises alt.2 windows), and adds the deducted area back to the main wall's net area so the conservation invariant holds. Cohort impact: cert 2636 cascade walls closes from 20.5595 → 20.0240 (spec-exact to 1e-3). Cascade (37) closes from 114.7067 → 114.1846 (Δ +0.0134 from a small thermal-bridging area rounding diff). Cert 2636 SAP shifts from -0.0055 → +0.0323 — joining the cohort cluster (all 7 ASHP certs now within +0.030 to +0.059 SAP). The current near-zero cancellation state for cert 2636 was hiding two opposite cascade errors (over-count walls + under-count η_space). This slice closes walls correctly; the remaining +0.03 SAP cluster across all 7 certs is the systematic PSR-denominator HLC×ΔT drift documented in the handover (not max_output, which BRE confirmed is 4.39 kW exactly). Zero regressions on Elmhurst hand-built fixtures, closed-cert Layer 4 1e-4 chain gates, or golden cert residual pins. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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| .vscode | ||
| applications | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| deployment/terraform | ||
| docs/adr | ||
| domain | ||
| epr_data_exports | ||
| etl | ||
| infrastructure | ||
| model_data/requirements | ||
| orchestration | ||
| recommendations | ||
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| 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