82b8a16b40
The 001431 corpus at `sap worksheets/heating systems examples/` now
has a permanent test module pinning cascade-vs-worksheet residuals
across all 41 populated heating-system variants. The corpus is a
controlled-variable test set — same dwelling (semi-detached, TFA 90 m²,
age G, W6 9BF, Elmhurst P960 worksheet format) under different heating
configurations — so every cascade-vs-worksheet residual is fully
attributable to the heating subsystem.
`test_heating_systems_corpus_residual_matches_pin` is parametrised by
variant folder name. Per variant it:
1. Extracts Block 11a (individual) or Block 11b (community) pins
from the P960 PDF — continuous SAP (`SAP value` row), total fuel
cost (255)/(355), CO2 (272/372/382/383), PE (286/386/486/483).
2. Routes the Summary PDF through ElmhurstSiteNotesExtractor →
EpcPropertyDataMapper.from_elmhurst_site_notes → cert_to_inputs
→ calculate_sap_from_inputs.
3. Asserts each of the four cascade outputs sits within an absolute
tolerance of the pinned residual.
Tolerances are tight (SAP ±0.001, cost ±£0.01, CO2 ±0.1 kg/yr, PE
±0.1 kWh/yr) — the *expected residual* moves toward 0 as heating-
cascade gaps close; the *tolerance* never widens. Per
[[feedback-zero-error-strict]] + [[feedback-golden-residuals-near-zero]].
Pins captured at HEAD `729ee29c` (post-S0380.128). All 41 pass.
Smallest residual: `solid fuel 8` +0.87 SAP / −£20 cost (closest to
closure). First negative ΔSAP: `community heating 6` −6.87 SAP / +£158
cost (heat-pump heat network — only variant where cascade UNDERshoots
the worksheet).
Extended handover suite at HEAD post-slice: **873 pass, 0 fail**
(was 832 + 41 new parametrised cases).
Pyright net-zero on new file (0 → 0).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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| .devcontainer | ||
| .github/workflows | ||
| .idea | ||
| .vscode | ||
| applications | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| deployment/terraform | ||
| docs/adr | ||
| 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