eea5d3a5a8
SAP 10.2 Table 4a electric boilers (PDF p.170) split across three distinct
Table 12a Grid 1 SH rows (PDF p.191), not one "direct-acting" family as the
stale TODO in `_table_12a_system_for_main` implied:
- 191 Direct-acting electric boiler -> "Direct-acting electric boiler (a)"
row: 7-hour 0.90, 10-hour 0.50 (NOT the 1.00/0.50 "Other direct-acting
electric heating" room-heater row).
- 193/194/195/196 Electric dry core / water storage boiler -> "Electric dry
core or water storage boiler" row: 7-hour 0.00 (charged wholly off-peak =
100% low rate, identical to the None fallback).
- 192 Electric CPSU -> Appendix F; left falling through to None (off-peak
low) until the Appendix-F high-rate cascade is implemented.
The enum + fractions already existed in table_12a.py; only the code->enum
mapping was missing. Resolves the TODO and pins the spec-correct 0.00 for the
storage boilers so 195 can't be mis-"fixed" up to a direct-acting fraction.
Forward guard, 0 corpus impact: storage boilers already billed 100% low via
the None fallback, and all corpus 191 certs are on standard tariff (Table 12a
off-peak split never fires). Corpus gauge unchanged 73.3% / MAE 0.774.
Pin: test_electric_boilers_191_195_map_to_distinct_table_12a_grid1_rows.
pyright strict gate not run locally (pyright not installed in this container).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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| .claude/skills | ||
| .devcontainer | ||
| .github/workflows | ||
| .idea | ||
| .vscode | ||
| applications | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| deployment/terraform | ||
| docs | ||
| domain | ||
| epr_data_exports | ||
| etl | ||
| harness | ||
| 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 | ||
| next_claude_prompt.txt | ||
| P960-0001-001431-2.pdf | ||
| package-lock.json | ||
| package.json | ||
| playground.py.local-backup | ||
| pyproject.toml | ||
| pyrightconfig.json | ||
| pytest.ini | ||
| README.md | ||
| run_lambda_local.sh | ||
| serverless.yml | ||
| Summary_001431-3.pdf | ||
| 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