1b183f9c86
SAP 10.2 Table 4a (PDF p.165) lists "Heat pumps" as category 4 for
SAP main-heating codes:
211-217 — ground/water source heat pumps
221-227 — air source heat pumps (224 = ASHP 2013+, COP 1.70)
521-527 — warm-air heat pumps
Cert 000565 Main 1 lodges `Main Heating SAP Code = 224` (ASHP 2013+)
with `PCDF boiler Reference = 0` — i.e. no PCDB Table 362 lookup is
possible. Pre-slice `_elmhurst_main_heating_category` returned None
on this path (the existing PCDB-Table-362-membership check failed),
falling through to the cascade's `_DEFAULT_PUMPS_FANS_KWH_PER_YR =
130` (incorrect — HP circulation pump's electricity is inside the
system COP per SAP 10.2 Table 4f line "Heat pumps", so the cascade
row is 0 kWh/year for category 4).
Single-line fix: after the existing PCDB-resolution branches, check
`mh.main_heating_sap_code in _HEAT_PUMP_SAP_MAIN_HEATING_CODES` and
return category 4 if so. New frozenset of HP codes (subset of the
existing `_ELECTRIC_SAP_MAIN_HEATING_CODES`).
Transient state at HEAD (cert 000565):
- main_heating_category: None → 4 ✓
- pumps_fans cascade: 255.0 → 125.0 kWh/yr (HP base 0 + flue 45 +
solar HW 80; MEV +127.5 kWh still missing — wiring lands in
S0380.102)
- sap_score (int): 29 ✓ EXACT preserved
- sap_score_continuous: 28.31 → 28.69 (transient drift +0.39 vs ws;
the previously-cancelling +130 over-count is gone, restoring the
MEV-under net negative — closes when S0380.102 lands)
Cohort safety: cohort certs 000474..000516 are gas-combi with
`sap_main_heating_code=None` (PCDB Table 105 boiler identified via
the index instead). No cohort cert affected. Cert 0380 + other
golden HP fixtures lodge category=4 via the API mapper, also
unaffected.
Per the spec citation in [[feedback-spec-citation-in-commits]] +
the standing TODO at mapper.py:4037-4043, this slice is the
category half of the coupled cert 000565 closure arc.
Pyright net-zero per touched file.
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