Slice 5a: the promotion. Replaces StubRebaseliner in production and collapses the
shadow runner into the rebaseliner (ADR-0013 amendment).
- CalculatorRebaseliner runs Sap10Calculator on every Property:
* sap_version < 10.2 -> Effective Performance IS the calculator output
(band via Epc.from_sap_score, CO2 kg->t, PEUI rounded), reason "pre_sap10".
* sap_version >= 10.2 -> Effective = lodged (API figures on-target), and the
calculator only logs divergence (SAP>0.5, PEUI/CO2 1%) as a validation signal.
* a calculator raise propagates -> batch aborts (ADR-0012); fix the cert at once.
- Rebaseliner.rebaseline gains property_id (for the divergence log).
- LoggingCalculatorShadow / the calculator_shadow seam removed from the
orchestrator; its divergence-comparison logic now lives in the rebaseliner.
- StubRebaseliner kept (signature updated) for orchestrator/repo unit tests.
The SapResult->EnergyBreakdown adapter + BillDerivation wiring (to populate the
bill block) follow once the appliances/cooking SapResult fields land.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Wire Sap10Calculator into PropertyBaselineOrchestrator as a non-load-bearing
shadow runner. For each property it scores the Effective EPC beside the
load-bearing Lodged/Effective write, catches any strict-raise -> log.error
(never aborts the batch), and on success log.warning's divergence from Lodged:
SAP |continuous - lodged| > 0.5; PEUI/CO2 > 1% relative (CO2 after kg->tonnes).
Every line is tagged with sap_version so SAP-10.2 signal separates from
older-spec drift (ADR-0010 Validation Cohort).
Per ADR-0013, Calculated SAP10 Performance is not a persisted third value-set:
effective = calculated in every baselining scenario, so the calculator IS the
mechanism that produces Effective Performance (the Rebaseliner). It runs in
shadow only while being hardened; when overrides/estimation land it is promoted
to drive Effective and the failure posture flips to abort (ADR-0012, calculator
now load-bearing). No table change.
- ADR-0013 + CONTEXT (Calculated SAP10 Performance / Effective Performance /
Rebaselining) record the decision.
- CalculatorShadow port + LoggingCalculatorShadow + Calculator protocol.
- FakeCalculatorShadow for orchestrator unit tests.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Aligns the composition with its entry point (the `ara_first_run` lambda +
`AraFirstRunTriggerBody`): clearer what the file does.
- orchestration/first_run_pipeline.py → ara_first_run_pipeline.py
- FirstRunPipeline → AraFirstRunPipeline; FirstRunCommand → AraFirstRunCommand
- test files renamed to match
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Final slice of ADR-0012: collapse the per-property read round-trips a batch
made (Baseline hydrated ~8 queries x 30 properties one at a time) into a
handful of per-table IN queries.
- EpcPostgresRepository: extracted a shared `_compose(rows)` from `get` (the
windows + floor-dim fetches are now passed in, not fetched inline), so both
`get` and the new `get_for_properties(property_ids)` build EpcPropertyData
from pre-fetched rows. `get_for_properties` fetches each child table once
(`WHERE epc_property_id IN ...`), groups in memory, and composes — load-whole
per ADR-0002.
- PropertyRepository.get_many(property_ids) -> Properties: one query for the
property rows + one bulk EPC hydration, composed in input order.
- BaselineOrchestrator / IngestionOrchestrator read the batch via get_many
instead of N x get.
- Ports + fakes gain the bulk methods.
The #1129 round-trip fidelity test stays green (the compose extraction is
behaviour-preserving). New tests: bulk hydration correctness + round-trips are
constant w.r.t. batch size (one-per-table, proven by query count). 123 pass;
pyright strict clean; AAA.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Replaces the handler's whole-pipeline Session (one transaction across all
three stages, connection pinned during Ingestion's external IO) with a
Unit-of-Work per stage (ADR-0012, added here). Each stage runs its batch in
one unit and commits once; any property raising aborts the batch and the
subtask fails noisily.
- BaselineOrchestrator(unit_of_work, rebaseliner): one unit for the batch,
commit once. Raise on a pre-SAP10 property leaves the unit uncommitted.
- IngestionOrchestrator(unit_of_work, epc_fetcher, geospatial_repo,
solar_fetcher): fetch/write split — phase 1 fetches the whole batch (EPC /
coords / solar) with NO unit open; phase 2 writes in one unit and commits.
The connection is never held during external IO. Geospatial S3 repo stays
injected (reference data, not transactional).
- Handler: module-scoped engine (pool reused across warm invocations) + a UoW
factory; whole-pipeline `with Session` gone. `build_first_run_pipeline`
composes on the factory. Source clients still behind the raising seam.
- ADR-0012 records the decision (per-stage boundary, all-or-nothing batch,
idempotent re-run, fetch/write split, module-scoped engine). Modelling stub
left untouched (no-op, no DB) per the ADR.
Tests: orchestrators on a shared FakeUnitOfWork (assert persisted batch +
exactly-once commit + no-commit-on-raise). New real-DB E2E integration test:
real PostgresUnitOfWork, Ingestion writes the EPC → Baseline reads it back
through the repo → re-run replaces, not duplicates (1 EPC row, 1 baseline row
after two runs). 121 pass in tests/; pyright strict clean; AAA.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Completes the First Run spine. Replaces the #1130 stub FirstRunPipeline
with the real three-stage composition and wires it into the handler.
- `FirstRunPipeline.run(command)` sequences Ingestion → Baseline →
Modelling, threading **only** `property_ids` between stages (and
`scenario_ids` into Modelling, off the command — never a prior stage's
output). Stages are injected behind thin `IngestionStage` /
`BaselineStage` / `ModellingStage` Protocols (the EpcFetcher/SolarFetcher
idiom), so the handler owns wiring and tests substitute fakes (ADR-0011).
- `ModellingOrchestrator` stub + `ScenarioRepository` / `MaterialsRepository`
seam ports — `run(property_ids, scenario_ids)` reads through repos, does
no scoring yet. Method shapes deferred to the Modelling per-service grills
(Scenario / Scenario Phase / Snapshot / Optimised Package / Plans are rich
— not pre-empted here).
- Handler delegates to the real pipeline via `build_first_run_pipeline`
(Postgres-backed repos off the session). The Ingestion source clients
(EPC API / Google Solar / geospatial S3) are isolated behind one
`_source_clients_from_env` seam that raises until the deploy/Terraform
config settles — out of scope for this slice. Subtask complete/failed +
CloudWatch URL still come from `@subtask_handler`.
Integration test (the criterion's centrepiece): wires REAL Ingestion +
REAL Baseline + stub Modelling through a shared fake EPC repo, with a
repo-backed PropertyRepo composing the Property from that slice. Proves
Baseline reads the very EPC Ingestion persisted — the through-repos
hand-off, no in-memory coupling. Plus a composition test pinning stage
order + only-property_ids threading.
TDD, one test → one impl. pyright strict clean; AAA layout. 116 pass in
the tests/ tree, no regressions.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Stage 2 of First Run. Establishes each Property's Baseline Performance
from persisted source data and writes it back — reads only from repos,
never a Fetcher or HTTP (ADR-0003), so it is byte-identical whether
Ingestion ran milliseconds ago or last week.
Domain (`domain/baseline/`):
- `Performance` VO — the four rated quantities: SAP / EPC Band / CO2 /
Primary Energy Intensity. `lodged_performance(epc)` reads them off the
EPC's recorded fields (PEUI = `energy_consumption_current`).
- `BaselinePerformance` (ADR-0004) — the paired `lodged` + `effective`
Performance + `rebaseline_reason`, plus the no-derivation part of the
energy block (`space_heating_kwh` / `water_heating_kwh`, off the RHI,
deterministic per ADR-0006). Both halves always populated.
- `Rebaseliner` port + `StubRebaseliner`: the re-score-on-override seam
(ADR-0011). SAP10 certs pass through (effective == lodged, reason
"none"); a pre-SAP10 cert raises `RebaselineNotImplemented` rather
than fabricating a plausible-but-wrong "none" — ML rebaselining is not
wired yet. Mirrors the repo's strict-raise culture.
Persistence: new `BaselineRepository` port + `BaselinePostgresRepository`
+ flat-column `baseline_performance` SQLModel (one row per Property). Per
ADR-0004's amendment this is a standalone table, NOT columns on the
retiring `property_details_epc`. Production migration is FE-owned
(Drizzle) — docs/migrations/baseline-performance-table.md.
Docs (grill-with-docs): corrected CONTEXT.md Lodged/Effective Performance
to Primary Energy Intensity (the term collided with its own _Avoid_ entry
under "heat demand") + fixed stale RHI field names; amended ADR-0004
Consequences for the standalone-table decision.
Fuel split + bills (rest of EPC Energy Derivation) deferred to a
follow-up — they need a Fuel Rates source (Ofgem-cap ETL) that does not
exist yet.
TDD, one test -> one impl: 7 tests (lodged read, rebaseliner pass-through
+ raise, orchestrator establish-and-persist + pre-SAP10 raise, Postgres
round-trip + absent). pyright strict clean; AAA layout.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Stage-2 entry point for the First Run use case. Adds the
`ara_first_run` Lambda package mirroring the `postcode_splitter`
template, its typed trigger contract, and a stub `FirstRunPipeline`.
- `AraFirstRunTriggerBody`: thin command of five fields — `task_id`,
`sub_task_id` (UUID, lifecycle), `portfolio_id`, `property_ids`,
`scenario_ids` (int business IDs). No `model_config` override, so
Pydantic's default `extra="ignore"` lets the FastAPI backend add
fields without breaking deployed lambdas. UPRNs / Scenario defs are
deliberately off the event — read from source-of-truth tables.
- Thin `handler.py`: validate-and-delegate only, via a named
`dispatch_first_run` seam (testable without the Lambda runtime).
Subtask status (in-progress/complete/failed) + CloudWatch log URL
come for free from the existing `@subtask_handler()` decorator.
- `FirstRunPipeline` (orchestration/) stub: `run(command)` receives the
validated command. Declares a structural `FirstRunCommand` Protocol
(the three business fields) that `AraFirstRunTriggerBody` satisfies,
so orchestration needs no application-layer import — rhymes with the
`EpcFetcher`/`SolarFetcher` Protocols on IngestionOrchestrator
(ADR-0011). Full Ingestion→Baseline→Modelling composition lands in
#1136.
- Dockerfile / requirements.txt / local_handler/ mirror postcode_splitter.
TDD: 7 new tests (trigger-body validation incl. forward-compat +
id-types, pipeline seam, handler delegation). pyright strict clean.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Stage 1 of the pipeline: per property, read its UPRN from the property row,
fetch its EPC, resolve coordinates from the Geospatial reference repo, thread
those into the Solar fetcher, and persist EPC + solar via repos. Fetchers never
call each other — the orchestrator threads the coordinate (ADR-0011). Coordinates
are reference data (deterministic from UPRN), resolved transiently to drive the
solar fetch rather than persisted per-property.
Depends on thin EpcFetcher/SolarFetcher Protocols (EpcClientService and
GoogleSolarApiClient satisfy them structurally). Unit-tested against fakes — no
DB, gov API, or network: persists EPC, threads coords into solar, skips
UPRN-less properties and skips solar when coordinates are absent. pyright clean.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Drive the contract for LandlordDescriptionOverridesOrchestrator.
get_col_to_description_mappings: given a list of UserAddress sharing
the same landlord_additional_info keys, return each key mapped to the
list of values found across all addresses.
Tests are red — the method still raises NotImplementedError.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Wires slice 1-5 primitives into a deployable splitter:
- orchestration/postcode_splitter_orchestrator.py: PostcodeSplitterOrchestrator
loads addresses via UserAddressRepository, groups by postcode via
iter_postcode_grouped_batches, persists each batch under
ara_postcode_splitter_batches/{task_id}/{subtask_id}/, creates a WAITING
child SubTask, and publishes an address2UPRN SQS message per batch.
- applications/postcode_splitter/: Lambda entrypoint. handler.py is decorated
with @subtask_handler() so the parent SubTask lifecycle is decorator-owned;
PostcodeSplitterTriggerBody validates the body. Dockerfile is the
python:3.11 Lambda base with the DDD-shaped source layers and no pandas.
- tests/orchestration/test_postcode_splitter_orchestrator.py: integration
test using moto S3 + moto SQS + in-memory SQLite that exercises the full
wiring against a fixture CSV spanning three postcode groups (one
oversize) and asserts child count, persisted inputs, queue bodies, and
dispatch order.
backend/postcode_splitter/ and .github/workflows/deploy_terraform.yml are
intentionally unchanged: the dockerfile_path flip is deferred until the
companion backend/address2UPRN/ migration is also ready.
Adds a primitive for creating a new WAITING SubTask under an existing
parent Task, routing all SubTask creation through the orchestrator
(replacing the legacy SubTaskInterface path used by the splitter).
Skips _cascade because a new WAITING child against an IN_PROGRESS
parent is a no-op under Task.recalculate_from_subtasks.
