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Contains projects associated to the development of modeling products
fa6974bdd9
RdSAP 10 §3.10.1 (PDF p.24) "Default U-values of the roof rooms":
> "The residual area (area of roof less the floor area of room(s)-in-
> roof) has a U-value from Table 16 : Roof U-values when loft
> insulation thickness is known according to its insulation thickness
> if at least half the area concerned is accessible, otherwise it is
> the default for the age band of the original property or extension."
Plus RdSAP 10 §3.9.1 step (d-e) (PDF p.21-22) — the Simplified A_RR
formula `12.5 × √(A_RR_floor / 1.5)` is the empirical estimator for
the total RR exposed shell; residual = A_RR − Σ lodged walls. The
worksheet applies this same formula to Detailed mode when the lodged
surface set has no roof-going entries (cert 000565 BP[0]:
12.5 × √(45/1.5) − (9.8 + 14.7) = 43.96 ≈ ws 43.97).
Pre-slice the cascade computed residual area ONLY in the Simplified
RR branch (via `_part_geometry`'s `rr_simplified_a_rr_m2` − rr_common
− rr_gable subtractions). The Detailed-RR branch in
`heat_transmission` iterated `rir.detailed_surfaces` and missed the
residual entirely. Cert 000565 routes all 5 BPs through Detailed mode
(the Elmhurst mapper translates Summary "Simplified" lodgements to
`SapRoomInRoofSurface` records when per-surface L×H is present), so
cascade total_external_element_area_m2 was 779.27 m² vs worksheet
(31) = 857.64 m² (Δ −78.37 m² → thermal_bridging cascade −11.76 W/K
under).
Slice span (1 file):
- `heat_transmission.py`: Detailed-RR branch adds residual area via
the §3.9.1 A_RR formula minus wall-going lodgements (gable_wall,
gable_wall_external, common_wall). Residual area contributes to
`rr_detailed_area` (→ part_external_area → (31) → thermal_bridging
multiplier) and to `roof` at `u_rr_default_all_elements`.
- Discriminator: residual fires only when no roof-going surface kinds
(slope, flat_ceiling, stud_wall) are lodged — true Detailed-mode
lodgements (cohort fixture 000516) lodge the entire roof shell
explicitly and have no residual.
Cert 000565 movement (HEAD `78c57c0d` → this slice):
- thermal_bridging_w_per_k: 116.89 → 129.35 ✓ vs ws 128.65 (Δ +0.70)
- total_external_area_m2: 779.27 → 862.34 ✓ vs ws 857.64 (Δ +4.70)
- roof_w_per_k: 34.64 → 63.72 (Δ −16.74 → +12.34)
- sap_score_continuous: 29.02 → 28.07 (Δ +0.51 → −0.44)
- sap_score (integer): 29 → 28 (temp regression
past 28.5 threshold)
- space_heating_kwh: −685 → +533
- main_heating_fuel: −403 → +321
- hot_water_kwh: ✓ 0 EXACT unchanged
Per user direction temporary continuous-SAP drift is acceptable when
fixing real spec-correct sub-component bugs; the absolute continuous-
SAP residual is now −0.44 (was +0.51) — slightly closer to zero
overall. The roof overshoot localises to:
- BP[4] Flat Ceiling 1 "Unknown PUR or PIR" lodgement (cascade 2.30
vs ws 0.15, over by +10.75 W/K) — Elmhurst-specific "Unknown +
known material" convention not yet wired
- BP[1] residual formula gives +3.68 m² over worksheet (Δ +1.29 W/K)
— Detailed-mode residual is spec-ambiguous for extensions with
non-2.45 m RR height; future slice may add a height-aware formula
Cohort safety: discriminator `has_roof_lodgement` filters out true
Detailed-mode lodgements (cohort fixtures 000474/000477/000480/
000487/000490/000516 all lodge slope/flat_ceiling/stud_wall surfaces).
Initial implementation broke 41 cohort pins; the discriminator
restores cohort behaviour exactly. Test baseline: 585 pass + 9
expected `000565` fails (was 585 + 8 — sap_score moved from passing
to failing during the slice's transient overshoot; expected per
user direction).
Pyright net-zero per touched file (test_summary_pdf_mapper_chain.py
13 → 13 preserved; heat_transmission.py 13 → 12 improved by −1).
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