6a4539f26b
Two second-main fuel errors mis-cost a dual-main dwelling whose two main systems burn different fuels (SAP 10.2 §10a worksheet (213) bills main 2 at its own fuel): 1. Off-peak/legacy scalar cost path (calculator.py + cert_to_inputs.py): main 2's kWh was priced at main 1's `space_heating_fuel_cost_gbp_per_kwh` scalar. Split main 1 vs main 2 and price main 2 at its OWN rate via the new `_main_2_space_heating_fuel_cost_gbp_per_kwh` (+ CalculatorInputs field). Scoped to a NON-electric second main (wood/oil/coal) — an electric second main keeps main 1's scalar (its off-peak Table 12a split is the deferred §10a slice; per-system splitting it regresses the off-peak electric cohort, certs 13 Parkers Hill / 34 Dunley Road). 0 corpus impact (no corpus cert has a non-electric second main on an off-peak meter). 2. Elmhurst Summary mapper (mapper.py): when §14.1 omits the Fuel Type cell, a fuel-fired second main (room-heater SAP code) inherited main 1's fuel. Derive it from the SAP code's Table 4a category (solid 631-636 -> house coal, gas -> mains gas, liquid -> oil) before the main-1 inherit, mirroring `_elmhurst_secondary_fuel_from_sap_code` (same modal sub-fuel caveat). Boiler codes (<601) still inherit main 1 (case 6 oil rads+UFH). simulated case 47 (electric room heaters + solid room heaters 633): our SAP 37.81 -> 55.09 vs Elmhurst current 57 (residual is the wood-vs-coal sub-fuel the Summary export does not carry). Corpus unchanged 72.5% / MAE 0.793; batch 0 raised / 0 diverge; 000565 e2e green. (mapper.py also carries an earlier, behaviour-free roof-window doc comment.) Spec-cited unit pins added (AAA). pyright not installed locally — strict type gate not run. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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| .claude/skills | ||
| .devcontainer | ||
| .github/workflows | ||
| .idea | ||
| .vscode | ||
| applications | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| deployment/terraform | ||
| docs | ||
| domain | ||
| epr_data_exports | ||
| etl | ||
| harness | ||
| infrastructure | ||
| model_data/requirements | ||
| orchestration | ||
| recommendations | ||
| repositories | ||
| sap worksheets | ||
| 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 | ||
| next_claude_prompt.txt | ||
| P960-0001-001431-2.pdf | ||
| package-lock.json | ||
| package.json | ||
| playground.py.local-backup | ||
| pyproject.toml | ||
| pyrightconfig.json | ||
| pytest.ini | ||
| README.md | ||
| run_lambda_local.sh | ||
| serverless.yml | ||
| Summary_001431-3.pdf | ||
| 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