8020854ab6
Pins the full API → cert_to_inputs → calculate_sap_from_inputs cascade
for each of the 7 ASHP cohort certs against the Elmhurst dr87
worksheet's continuous SAP. Tolerance is 0.07 (NOT 1e-4 like the
boiler cohort) — see HANDOVER_CERT_0380_MIT_CASCADE.md:
- BRE web confirmed max_output_kw matches cascade (4.39 for
Mitsubishi PCDB 104568, 3.933 for Daikin PCDB 102421).
- Cascade (39) annual HLC matches worksheet at 4 dp exact for
certs 0380, 2225.
- Back-solving worksheet η_space implies ~0.15% drift in
Elmhurst's internal η_space interpolation precision (likely
a vendor rounding convention not in public SAP 10.2 spec).
The 7-cert cohort clusters within +0.030..+0.060 SAP — this is the
spec-precision floor for the publicly-documented cascade.
At rounded (integer SAP) precision, all 7 cascade integers match
the lodged values exactly (residual = 0, pinned in
`_GOLDEN_EXPECTATIONS` per slice 102f-prep.11).
Cohort summary:
0380 88.5698 vs 88.5104 Δ=+0.059 Mitsubishi PUZ-WM50VHA
0350 84.1825 vs 84.1367 Δ=+0.046 Mitsubishi PUZ-WM50VHA
2225 88.8362 vs 88.7921 Δ=+0.044 Mitsubishi PUZ-WM50VHA + PV
2636 86.2964 vs 86.2641 Δ=+0.032 Mitsubishi PUZ-WM50VHA + cantilever
3800 86.1900 vs 86.1458 Δ=+0.044 Mitsubishi PUZ-WM50VHA
9285 84.1871 vs 84.1369 Δ=+0.050 Mitsubishi PUZ-WM50VHA
9418 84.6601 vs 84.6305 Δ=+0.030 Daikin Altherma EDLQ05CAV3 ("24" duration)
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