c40679d1e1
`u_floor` defaulted to the SOLID branch for age bands C+ when both `construction` (int code) and `description` were None, regardless of whether the bp's own `floor_construction_type` field said "Suspended timber". This produced U=0.60 for cert 001479 Main vs the worksheet's U=0.65 — a -0.05 W/m²K delta × 30.45 m² → -1.52 W/K of fabric loss shortfall. Fix: in `heat_transmission_section_from_cert`, prefer the bp's `floor_construction_type` string over the global `epc.floors[]. description` when computing the per-bp floor U. The bp-level field is the per-part lodgement Elmhurst surfaces in §3 / §9 of the Summary; the global `epc.floors` list is often empty when the mapper sources data from a Summary PDF rather than the full RdSAP API JSON. Impact on cert 001479 Summary → mapper → cascade SAP delta: BEFORE Slice 88: +0.2290 (floor U 0.60 vs target 0.65) AFTER Slice 88: +0.0898 (floor exact match; only roof gap left) Floor W/K breakdown for cert 001479 (mapper path): was: 21.6480 target 23.1705 delta -1.5225 now: 23.1705 target 23.1705 delta +0.0000 ✓ EXACT Cohort cascade pins remain GREEN (66 of 66) — the cohort hand-builts already set `floor_construction_type` on their Main bp via the Slice 72/75/78/82/85 Cat A bulk updates, so the new code path applies the same suspended-timber branch that previous paths reached via either explicit `floor_construction` int codes or the age-band default (cohort certs are all age B which is in `_SUSPENDED_TIMBER_DEFAULT_BANDS`, so they hit the suspended branch either way; cert 001479 is age C and needs the explicit string). Pyright net-zero on heat_transmission.py (13 → 13 errors). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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|---|---|---|
| .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