b8f35af902
Closes 23 of 45 mapper-vs-hand-built load-bearing divergences by populating fields the Elmhurst mapper extracts from Summary_000487. pdf but the original hand-built left at their `make_minimal_sap10_ epc` / dataclass-default values. Every change is cascade-equivalent — none alter `_FIXTURE_PINS["000487"]` SapResult fields (all 11 1e-4 pins remain GREEN against worksheet `SAP value 61.6431`). Mirrors the Slice 64 / 72 / 75 pattern. 000487-specific deltas: - `wall_thickness_measured=True` on **both** bps (Summary §7 lodges measured thickness for Main and Ext1 on this cert). - Floor descriptive: Main "Ground floor" + suspended timber; Ext1 "Above unheated space" + suspended timber (the cert's `is_exposed_floor=True` for the lowest Ext1 floor). - `dwelling_type="Enclosed Mid-Terrace house"`, `built_form="Enclosed Mid-Terrace"` — the Summary distinguishes Enclosed from plain Mid-Terrace; mapper preserves the distinction. - `shower_outlets=ShowerOutlets(shower_outlet_type="Electric shower")` — 000487 lodges 1 instantaneous electric shower (vs Non-electric on 000477/000480 cohort certs). - `extensions_count=1`, plus standard top-level booleans, `number_of_storeys=3`, ventilation zero counts. Diff count: 45 → **22**. Remaining diffs are structural / encoding- choice: - RIR `detailed_surfaces` ordering mismatch + per-surface encoding (handbuilt pins explicit `u_value=0.86` on gable_wall_external; mapper extracts insulation_thickness=100 + mineral_wool) — Slice 79 - Alt-wall `wall_construction=8 (SAP10 Park-home)` is mislabeled in the hand-built — Elmhurst's "TI Timber Frame" maps to SAP10 code 5 (per `_ELMHURST_WALL_CODE_TO_SAP10`); mapper produces the correct code 5 — Slice 79 - `sap_windows: LEN 5 vs 2` — Slice 80 11 cohort 000487 cascade pins still GREEN; pyright net-zero. 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