1491899412
Adds the single missing dict entry that lets cert `pcdb 3` cascade:
`_ELMHURST_MAIN_FUEL_TO_SAP10["Bulk LPG"] = 27`
API code 27 = "LPG (not community)" — routes via:
- `API_FUEL_TO_TABLE_12[27] = 2` (SAP 10.2 Table 12 bulk LPG: £62
standing, 6.74 p/kWh, 0.241 CO2, 1.141 PE; spec PDF p.189)
- `API_FUEL_TO_TABLE_32[27] = 2` (RdSAP 10 Table 32 bulk LPG: £70
standing, 7.60 p/kWh; spec PDF p.95)
Pre-slice the mapper produced `main_fuel_type=''` for any Elmhurst
fixture lodging "Bulk LPG" as fuel type, so the cascade strict-raised
`MissingMainFuelType` per S0380.132. The legacy `"LPG bulk"` label
(different word order) maps to API code 6 = wood logs — a pre-existing
oddity unexercised by any live fixture; left untouched per
[[feedback-bigger-slices-for-uniform-work]] (different label, different
fix).
Cascade closure `pcdb 3` (Vokera Linea LPG combi 83.10 %, PCDB index
8262, no cylinder, 18-hour tariff) — EXACT on first try across all 4
metrics:
cascade SAP_c = 49.2953 worksheet = 49.2953 Δ = +0.0000
cascade cost = £1165.81 worksheet = £1165.81 Δ = +0.0000
cascade CO2 = 3367.95 worksheet = 3367.95 Δ = +0.0000
cascade PE = 13936.60 worksheet = 13936.60 Δ = +0.0000
Closure on first try because the cascade was already fully wired for
the gas/oil/LPG path; the Elmhurst label was the only gap. Moves
pcdb 3 out of `_BLOCKED_BY_MISSING_MAIN_FUEL_TYPE` into `_EXPECTATIONS`
at ±0.0000.
Blocked tier now: 15 variants (community heating × 5, electric storage
11-14, no system, oil 2-6).
Tests:
- test_elmhurst_main_fuel_to_sap10_maps_bulk_lpg_to_api_code_27
- corpus pin: pcdb 3 expected residuals = ±0.0000 on all 4 metrics
912 pass / 0 fail; pyright net-zero 43 → 43.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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| .github/workflows | ||
| .idea | ||
| .vscode | ||
| applications | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| deployment/terraform | ||
| docs | ||
| 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