f502db8c74
The end-to-end production cascade `from_api_response → cert_to_inputs →
calculate_sap_from_inputs` now hits cert 001479's worksheet continuous
SAP 69.0094 at abs < 1e-4 (was +0.000584). Two fixes:
1. API mapper: `from_rdsap_schema_21_0_{0,1}` computes `total_floor_
area_m2` as Σ per-bp `sap_floor_dimensions[*].total_floor_area.value`
(cert 001479: 30.45+30.77+5.37+1.92 = 68.51), not the lodged scalar
(rounded integer 69). `water_heating_from_cert` reads `epc.total_
floor_area_m2` directly for occupancy N (Appendix J), which propagates
to HW kWh (+6.31 → ~0), Appendix L lighting (+0.98 → 0), and internal
gains (+25.72 W·months → 0).
2. Cascade window area rounding per RdSAP 10 §15 "Rounding of data"
(p.66): "All element areas (gross) including window areas: 2 d.p."
`solar_gains.py` and `internal_gains.py` now round `w * h` to 2 d.p.
to match the existing `heat_transmission.py` pattern (line 344).
Closes the residual solar gains delta (+1.50 W·months → 0) that
became dominant once TFA was fixed.
Re-pinned 5 golden cert residuals where TFA + area rounding shifted
output: 0240 (SAP -14→-15, PE +14.6650→+17.8450, CO2 +0.8060→+1.0097),
6035 (PE +48.2971→+49.5139, CO2 +1.1016→+1.1423), 8135 (PE -2.4194→
-2.4072, CO2 -0.0198→-0.0195), 2130 (PE -38.1521→-38.1666), 0390
(PE +1.6837→+1.6962, CO2 +0.0637→+0.0639).
New test: `test_api_001479_full_chain_sap_matches_worksheet_pdf_
exactly` formalises Layer 4 of the validation stack as a 1e-4 gate.
Pyright net-zero (mapper.py 33).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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||
|---|---|---|
| .devcontainer | ||
| .github/workflows | ||
| .idea | ||
| .vscode | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| docs | ||
| epr_data_exports | ||
| etl | ||
| infrastructure/terraform | ||
| model_data/requirements | ||
| packages | ||
| recommendations | ||
| scripts | ||
| services | ||
| sfr/principal_pitch | ||
| survey_report | ||
| utils | ||
| .coveragerc | ||
| .dockerignore | ||
| .gitignore | ||
| __init__.py | ||
| AGENTS.md | ||
| 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_backlog.sh | ||
| 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