b2b6f8e954
The Elmhurst Summary §15.1 lodges "Cylinder Size: Value known" with the measured volume in the "Cylinder Volume (l)" line — the Summary-path equivalent of the gov-API "Exact" descriptor. The mapper had no entry for "Value known" so `_elmhurst_cylinder_size_code` raised UnmappedElmhurstLabel, and even once mapped the measured volume was never threaded through, so the cascade dropped the cylinder storage loss (~468 kWh/yr) from (219) water heating on every measured-volume-cylinder Summary. Per RdSAP 10 §10.5 Table 28 (p.55) a measured cylinder volume is used directly. Map "Value known" → cascade code 6 (Exact) and thread the §15.1 "Cylinder Volume (l)" value into SapHeating.cylinder_volume_measured_l, which `_cylinder_volume_l_from_code` (cert_to_inputs.py:5281) already reads for code 6 — mirroring the gov-API path (mapper.py:1575/1885). Pins simulated case 39 (P960-0001-001431): an age-A mid-terrace on direct- acting electric room heaters (SAP code 691, cat 10, control 2602) with electric-immersion DHW off a 117 L "Value known" cylinder. The full extractor→mapper→calculator cascade now reproduces the worksheet's SAP-rating block EXACTLY — SAP value 36.6365 (band F) and (272) CO2 2056.0731 kg/yr, with (219) water heating 2637.5049 and (255) total energy cost 1802.0039. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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| applications | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| deployment/terraform | ||
| docs | ||
| domain | ||
| epr_data_exports | ||
| etl | ||
| harness | ||
| infrastructure | ||
| model_data/requirements | ||
| orchestration | ||
| recommendations | ||
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| sap worksheets | ||
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| sfr/principal_pitch | ||
| survey_report | ||
| tests | ||
| utilities | ||
| utils | ||
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| __init__.py | ||
| ara_backend_design.md | ||
| BaseUtility.py | ||
| CLAUDE.md | ||
| conftest.py | ||
| CONTEXT.md | ||
| devcontainer.sh | ||
| Dockerfile.test | ||
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| Makefile | ||
| MEMORY.md | ||
| next_claude_prompt.txt | ||
| package-lock.json | ||
| package.json | ||
| pyproject.toml | ||
| pyrightconfig.json | ||
| pytest.ini | ||
| README.md | ||
| run_lambda_local.sh | ||
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| test.requirements.txt | ||
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| 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