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Contains projects associated to the development of modeling products
78c57c0dc7
RdSAP 10 §5.11.3 + Table 17 (PDF p.42-43) "Roof room U-values when
insulation thickness is known". Column (3b) "Stud wall — PUR or PIR
optional" 400 mm row → 0.10 W/m²K. Cert 000565 Summary §8.1 BP[2] Ext2
(Detailed) lodges:
Stud Wall 2 2.00 × 2.00 400+ mm PUR or PIR Default U=0.10
Pre-slice three coupled bugs silently dropped the lodgement, routing
the cascade through the uninsulated Table 17 row 0 (U=2.30) — over-
counting Stud Wall 2 by (2.30 − 0.10) × 4 m² = +8.80 W/K on roof:
1. **Extractor regex** `_RIR_INSULATION_THICKNESS_RE = ^\d+\s*mm$`
failed to match the "400+ mm" bucket-cap form (Table 17's largest
tabulated row is annotated with a trailing "+" in the Summary).
2. **Extractor insulation_type allow-list** `("Mineral or EPS",
"PUR", "PIR")` failed to match the disjunction "PUR or PIR" — the
actual Summary form when the assessor doesn't distinguish PUR from
PIR. (Both columns Table 17 column (b) anyway.)
3. **Mapper thickness parser** `_elmhurst_rir_insulation_thickness_mm`
used the same `^\d+\s*mm$` regex — also failed on "400+ mm".
Plus a fourth coupled fix: the cascade's `_is_rigid_foam` checked a
frozenset `{"pur", "pir", "rigid"}` that didn't include the canonical
mapper-side code "rigid_foam" — even if the mapper translated "PUR or
PIR" → "rigid_foam", the cascade would route to column (a) mineral-
wool instead of column (b) rigid-foam.
Slice span (4 layers):
1. **Extractor regex** — `^\d+\+?\s*mm$` matches both "100 mm" and
"400+ mm".
2. **Extractor allow-list** — add "PUR or PIR" alongside individual
"PUR" / "PIR" + "Mineral or EPS".
3. **Mapper** — `_RIR_INSULATION_TYPE_TO_SAP10` canonicalises all
rigid-foam strings to "rigid_foam"; thickness parser regex matches
"400+ mm" → 400 mm int.
4. **Cascade** — `_RR_RIGID_FOAM_INSULATION_TYPES` adds "rigid_foam"
alongside the legacy "pur"/"pir"/"rigid" aliases.
Cert 000565 movement (HEAD `23aaa4fa` → this slice):
- cascade BP[2] Ext2 Stud Wall 2 U: 2.30 → 0.10 ✓ EXACT vs ws 0.10
- cascade roof_w_per_k: 43.44 → 34.64 (Δ−7.94 → Δ−16.74)
- sap_score: 29 ✓ EXACT unchanged
- sap_score_continuous: 28.81 → 29.02 (Δ+0.26 → Δ+0.51)
- space_heating_kwh: −427 → −685
- main_heating_fuel: −251 → −403
- hot_water_kwh: ✓ 0 EXACT unchanged
Closing one spec-correct sub-component while others remain non-spec-
correct drifts continuous SAP further; per user direction temporary
drift is acceptable as long as we're fixing true intermediate-value
problems — once every sub-component is spec-correct, the continuous
SAP error closes to zero by construction. The remaining −16.74 W/K
roof gap localises to:
- BP[0/1/3] missing RR residual area for Detailed-RR mode (§3.10.1
spec — cascade only handles Simplified mode today); +27.85 W/K
closure when wired.
- BP[4] Flat Ceiling 1 lodges "Unknown thickness, PUR or PIR" → ws
U=0.15; cascade over-counts at 2.30 (uninsulated). Elmhurst's
"Unknown PUR or PIR" → 200 mm convention is non-spec; the spec-
correct path falls back to Table 18 col 4 default (`u_rr_default
_all_elements`). Separate diagnostic slice.
Cohort safety: 21 other Elmhurst Summary fixtures lodge no RIR detailed
surfaces with "400+ mm" or "PUR or PIR" (modal cohort uses As Built /
None / no detailed surfaces). Existing "Mineral or EPS" tests at
`test_u_rr_stud_wall_table17_col3a_mineral_wool_100mm_returns_0_36`
remain green — the new aliases extend rather than replace.
Test baseline: 585 pass + 8 expected `000565` fails (was 583 + 8; +2
new tests). Pyright net-zero per touched file (0/32/1/65/13 preserved).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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| .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