e2d9f77d0f
The 000474 / 000477 / 000487 fixtures lodged sap_windows without an explicit u_value, relying on make_window's default u_value=2.8 (raw, pre-curtain-resistance). PDF lodges TWO window types per fixture: - Windows 1 (g_⊥=0.72): post-2002 double, raw U=2.0 → U_eff=1.8519 - Windows 2 (g_⊥=0.76): pre-2002 double, raw U=2.8 → U_eff=2.5180 - (000487 Windows 2 special: post-2022, raw U=1.4 → U_eff=1.3258) Lodging all windows at u_value=2.8 over-counted window heat loss (LINE_27/LINE_33) by 1.5-3% on mixed-glazing fixtures. The previous test_section_3 LINE_33 pin passed because it used a pre-computed WINDOW_AVG_RAW_U_VALUE constant rather than cert-derived sap_windows. Impact on `sap.space_heating_kwh_per_yr` vs PDF: fixture | before | after | gap before | gap after --------|------------|------------|------------|---------- 000474 | 10765.85 | 10615.86 | +152.99 | +3.00 (-98%) 000477 | 10318.34 | 10106.89 | +207.14 | -4.31 (-98%) 000480 | 12397.99 | 12397.99 | -0.58 | -0.58 (unchanged; all windows raw 2.8) 000487 | 12606.95 | 12303.35 | +1772.17 | +1468.57 (RR defect remains) 000490 | 11184.06 | 11184.06 | +0.78 | +0.78 (unchanged) 000516 | 12372.62 | 12372.62 | -37.70 | -37.70 (unchanged) The 000474 / 000477 cascade biases collapse by 98% — remaining 3-4 kWh residuals are precision-level and likely propagate from §4 HW or §7 T_i drift (sub-0.1°C). 000487 still 13.6% over because the RR lodgement defect (no detailed_surfaces, missing exposed_floor on Ext1, missing roof_insulation, U=0.86 second gable variant) is a separate slice. Cascade pin count stays at 48 fail / 18 pass because abs=1e-4 is tight — 3 kWh > 1e-4. But the underlying numeric residual dropped 50×. Subsequent pins (main_fuel, ecf, cost, sap_continuous) will also tighten as this cascade flows downstream. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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| .idea | ||
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| 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