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Contains projects associated to the development of modeling products
59de805e63
RdSAP 10 §3.9.2 step (b) (PDF p.23) verbatim:
"Software calculates the area of each gable or adjacent wall by
using the equation:
A_RR_gable = L_gable × (0.25 + H_gable) − [(H_gable − H_common_1)² / 2
+ (H_gable − H_common_2)² / 2]"
Step (d):
A_RR_final = A_RR_wall − (Σ A_common + Σ A_gable + Σ A_party
+ Σ A_sheltered + Σ A_connected)
The spec equation is signed and applies for all L > 0 — including
H_gable = 0. When the gable is shorter than the common walls the
correction term `(H_gable − H_common)² / 2` exceeds the
L × (0.25 + H_gable) term, producing a negative A_RR_gable.
Elmhurst's worksheet evaluates the equation literally; the negative
value adjusts A_RR_final upward via step (d) without billing a
physical wall area.
Cert 000565 §8.1 lodges Ext3's RR (Simplified Type 2) with an
absent Gable Wall 2:
Gable Wall 1 L=9.00 H=7.00 Exposed U=0.45
Gable Wall 2 L=4.00 H=0.00 U=0.00 ← lodged but H=0
Common Wall 1 L=5.00 H=1.50 U=0.45
Common Wall 2 L=7.50 H=0.30 U=0.45
Spec equation for Gable Wall 2:
A_gable_2 = 4 × (0.25 + 0) − (0 − 1.5)²/2 − (0 − 0.30)²/2
= 1.0 − 1.125 − 0.045 = −0.17 m²
Worksheet (30) Ext3 residual = 17.35 m² back-solves exactly:
A_RR_shell = 12.5 × √(32.0 / 1.5) = 57.7350
Σ walls (incl. -0.17 absent gable) = 40.3850
residual = shell − walls = 17.3500 ✓ 4 d.p.
Pre-slice the mapper had two clamps that together dropped the
spec-computed −0.17 m² adjustment:
mapper.py:3350 `if length_m <= 0 or height_m <= 0: return None`
→ filtered out any H=0 surface
mapper.py:3443 `area_m2 = max(0.0, length_m * (0.25 + H) − correction)`
→ clamped negative gable areas at 0
Combined the cascade computed residual = 17.18 m² (cascade UNDER
by 0.17). Plus a related secondary `if height_m > h` filter on the
correction sum that masked the all-common-walls-taller case.
3-layer fix:
1. `datatypes/epc/domain/mapper.py` `_map_elmhurst_rir_surface`:
- Split the early-return filter: drop only when L<=0 (no wall),
OR when H<=0 AND not (Simplified Type 2 with common walls).
- Apply the spec gable-area formula to BOTH `gable_wall` (party
default) and `gable_wall_external` kinds in Simplified Type 2
(the U-value routing differs by kind, but the area equation
is the same).
- Remove `max(0.0, ...)` clamp so the signed result reaches the
cascade.
- Remove `if height_m > h` correction-sum filter (spec applies
the full square unconditionally).
2. `domain/sap10_calculator/worksheet/heat_transmission.py` per-
surface loop:
- `gable_wall` branch: skip `party += 0.25 × area` when area < 0
(wall doesn't exist physically) but still add the signed area
to `rr_walls_in_a_rr_area` so the residual deduction in step (d)
grows by |area|.
- `gable_wall_external` branch: same skip pattern for `walls +=
u × area` and `rr_detailed_area += area`.
Cohort safety: only cert 000565 Ext3 hits this in the corpus. All
other cohort certs are Type 1 RR (no common walls, formula gives
the same answer) or have all gables H > 0. The cascade's per-element
test pins (Ext1's Connected gable + Exposed gable, Ext4's Detailed
RR) unchanged.
Cert 000565 cascade snapshot (HEAD
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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