5a74897fed
`_separately_timed_dhw` returned True for any boiler+cylinder+from-main
cert, applying the SAP 10.2 Table 2b note b) ×0.9 temperature-factor
reduction unconditionally. For the lpg-boiler "before" worksheet (pre-
1998 LPG boiler SAP code 115 + 210 L cylinder, NO cylinder thermostat,
control 2113 "Room thermostat and TRVs" — no programmer) this dropped
the (53) temperature factor to 0.702 (= 0.60 × 1.3 × 0.9) where the
worksheet lodges 0.78 (= 0.60 × 1.3), under-counting cylinder storage
loss (55) by ~119 kWh/yr and over-rating SAP by ~0.25.
RdSAP 10 §10.5 (PDF p.57) "Hot water separately timed":
No programmer, pre-1998 boiler → No
Programmer, pre-1998 boiler → Yes
Post-1998 boiler → Yes
DHW is therefore NOT separately timed only when a pre-1998 boiler is
paired with a no-programmer control. Add the two SAP 10.2 Table 4c(2) /
Table 4b lookups (controls without a programmer = {2101, 2103, 2111,
2113}; pre-1998 gas/LPG boilers 110-119 + oil 124/125/128) and return
False for that combination; every other boiler+cylinder cert keeps the
separately-timed default, so the change is confined to old low-control
stock and the heating corpus + goldens are unchanged.
Effect: the full chain (Summary PDF → extractor → mapper → cert_to_inputs
→ calculator) now reproduces the lpg-boiler worksheet's §11a unrounded
SAP -6.6499 at abs < 1e-4 (was -6.4013). Full regression suite green bar
the 3 pre-existing unrelated fails.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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|---|---|---|
| .devcontainer | ||
| .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 | ||
| 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 | ||
| 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