4ce2a71871
Make run_modelling_e2e the single script that does everything for a portfolio, so the 291-property run needs one invocation with per-property recovery (no all-or-nothing chunking): - On --persist, a lodged-EPC Property now also gets its Baseline Performance row written via PropertyBaselineOrchestrator (per Property, so one bad cert does not abort the batch). Predicted (EPC-less) Properties have no lodged figures, so they get a Plan but no baseline row. - The run CSV gains api_sap (register) vs baseline_sap (calculator) + sap_delta, so calculator-vs-API divergence is reviewable per property. Fill the off-catalogue overlay for the measures the live material catalogue cannot price, so they stop crashing the run: - double_glazing (£550/window) and secondary_glazing (£400/window): priced per window (the generator multiplies by single-glazed window count, matching the legacy window_glazing). Grounded in 2025/26 UK installed costs; per-window is the right unit for windows (fixed per-unit install dominates) — per-m2 fits walls/floors, not glazing. - gas_boiler_upgrade / system_tune_up / system_tune_up_zoned: these are priced off the heating rate sheet (Products()), with get() reading the catalogue only for an id — so the overlay entry exists to satisfy that lookup (material_id stays None, as with ASHP); the rate sheet remains authoritative. Validated on a 12-property sample (incl. a secondary-glazing case and a SAP-Schema-16.2 cert): 12/12 baseline rows + plans, 0 errors. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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| .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