From e12397c7817f48bf74fe2be763605e92948233b5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mar=C3=ADa=20Juaristi?= <127882282+juaristi22@users.noreply.github.com> Date: Fri, 11 Sep 2026 12:42:47 +0200 Subject: [PATCH 01/10] Re-pin the UK national target surface to Chronicle c6f9361 (#890) The national feed moves from ec7169b to c6f9361, the merge of chronicle #258 on top of #255: the transport and energy packages #890 binds (HMRC hydrocarbon oils, OBR fuel duty by vehicle category, DESNZ pump prices, DfT licensed cars by fuel type, Scotland/Wales/NI bus finance, ORR rail finance and fares index, NEED consumption, Ofgem cap levels by period and region, ONS Consumer Trends COICOP expenditure, NTS0313/NTS0621 bus-use frequency). 138,847 rows; the consumer-fact schema digest moved with the new quarter and week period types. No existing national reference resolves a different fact or value: 415 active, 7 deferred, 8 signed out, and the compile-parity receipts regenerate unchanged; only the membership's feed label moves. The local census, validation levels and local references keep their separately reviewed ec7169b pin, so the local regeneration test now reads its own default file (.codex-work/consumer_facts_uk_local.jsonl) instead of falling back to the national one; the runbook records both locations. Co-Authored-By: Claude Fable 5.1 --- changelog.d/uk-chronicle-repin-890.changed.md | 1 + docs/uk-chronicle-feed-repin.md | 13 +++++++++++++ .../build/uk/national_chronicle_feed.json | 10 +++++----- .../build/uk/target_reference_membership.json | 2 +- .../tests/test_uk_calibration_seam_driver.py | 11 ++++++----- .../tests/test_uk_national_chronicle_feed.py | 10 +++++----- .../tests/test_uk_target_references.py | 18 +++++++++++++++--- 7 files changed, 46 insertions(+), 19 deletions(-) create mode 100644 changelog.d/uk-chronicle-repin-890.changed.md diff --git a/changelog.d/uk-chronicle-repin-890.changed.md b/changelog.d/uk-chronicle-repin-890.changed.md new file mode 100644 index 000000000..2ceb2cf01 --- /dev/null +++ b/changelog.d/uk-chronicle-repin-890.changed.md @@ -0,0 +1 @@ +Re-pin the UK national target surface to Chronicle `c6f9361` (#890, from `ec7169b`). The artifact carries the 16 packages of chronicle #254/#255 and the six of #257/#258 (138,847 rows, +7,397): HMRC hydrocarbon oils quantities, OBR fuel duty receipts by vehicle category, DESNZ pump prices, DfT licensed cars by fuel type, Scotland/Wales/NI bus finance, ORR rail finance and fares index, NEED consumption tables, Ofgem cap levels for every charge restriction period and region, ONS Consumer Trends COICOP expenditure with `coicop` as a row dimension on both sheets, and NTS0313/NTS0621 frequency of local bus use. The chronicle consumer-fact schema digest moved (`76ac268e…` → `72ad3149…`) because `quarter` and `week` period types were added; no existing UK national reference resolves a different fact or value (415 active, 7 deferred, 8 signed out, unchanged), so the committed references and membership regenerate byte-identical apart from the feed identity. The local census, local validation levels and local reference surfaces keep their separately reviewed `ec7169b` pin. Adds `tools/vendor_uk_ledger_facts.py` with `uk/ledger_fact_vendor_selections.json`: pinned Chronicle facts the spine stages and target diagnostics consume are now copied verbatim into per-concern resources (`dft_bus_value_anchors.json`, `road_fuel_anchors.json`, `licensed_cars_fuel_type.json`, `need_energy_facts.json`, `ofgem_price_cap_facts.json`, `nts_bus_use_frequency.json`, `devolved_bus_finance.json`, `orr_rail_facts.json`, `ons_household_expenditure_facts.json`), each recording the feed identity it was taken from and regenerating byte-identical from the pinned feed under test; no value in a vendored resource is derived. diff --git a/docs/uk-chronicle-feed-repin.md b/docs/uk-chronicle-feed-repin.md index 064abd3b3..40749f9a3 100644 --- a/docs/uk-chronicle-feed-repin.md +++ b/docs/uk-chronicle-feed-repin.md @@ -8,6 +8,13 @@ update does not authorize changes to local census membership or values. Rebuild the complete UK bundle and consumer artifact in `PolicyEngine/chronicle` at the declared commit. Keep the resulting `consumer_facts.jsonl` and `manifest.json` together; do not commit either file. +Because the two surfaces are pinned independently, each has its own default +location under `.codex-work`: the national feed at +`.codex-work/consumer_facts_uk.jsonl` + `consumer_facts_uk_manifest.json` +(also mirrored as the artifact directory `.codex-work/uk-artifact/` for the +calibration runner), the local feed at `.codex-work/consumer_facts_uk_local.jsonl` ++ `consumer_facts_uk_local_manifest.json`. After a national-only re-pin the +local files stay at the local pin's artifact. Verify both SHA-256 digests and the manifest's `facts_sha256`, row count, and schema version. For a national update, update @@ -32,6 +39,12 @@ The national calibration runner refuses a feed whose facts or manifest digest differs from its committed pin. `--allow-unpinned-feed` is an explicit diagnostic override and is recorded in the run manifest; it is not a re-pin procedure. +After the `c6f9361` national re-pin (#890), the national surface carries the +chronicle #254/#255 and #257/#258 transport and energy packages; the local +surface stays on `ec7169b`. Vendored per-concern copies of pinned facts for the +spine stages are regenerated with `tools/vendor_uk_ledger_facts.py` from the +national feed (`uk/ledger_fact_vendor_selections.json` names them). + After the `ec7169b` re-pin, census household targets use the same Chronicle compile path as every other bound UK local family. The OA ladder now supplies geography assignment and diagnostic household dispersion only; it no longer diff --git a/packages/microcosm-build/src/microcosm/build/uk/national_chronicle_feed.json b/packages/microcosm-build/src/microcosm/build/uk/national_chronicle_feed.json index 139a234f5..9c7cd8ba5 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/national_chronicle_feed.json +++ b/packages/microcosm-build/src/microcosm/build/uk/national_chronicle_feed.json @@ -3,14 +3,14 @@ "country": "uk", "scope": "national_calibration", "source_repo": "PolicyEngine/chronicle", - "source_commit": "ec7169b5db40b9f54117c80f70f14efc1dd0fedd", + "source_commit": "c6f9361492056b9fab7b8535a2be77eb2b6c93bb", "build": "build-bundle --suite uk -> build-consumer-artifact", "artifact_schema_version": "policyengine_ledger.consumer_artifact.v2", "consumer_fact_schema_versions": [ "ledger.consumer_fact.v1" ], - "consumer_fact_schema_sha256": "76ac268e626c86146cee51193e0cbecbb197ddbf3bf410156fe7da7c0edae3ad", - "fact_row_count": 131450, - "facts_sha256": "4a50ee9568a01bbb57f73d927084ed6b4b9e52249b51a2338455874ae6e382b5", - "manifest_sha256": "a95d0ee9f87f36947eaecdb3de29cf81a91e47ccaa822fed42da677eedca877f" + "consumer_fact_schema_sha256": "72ad3149564f8aab3e9bb6de5ea25950c8e19a3e8402e2cdcfc52d250bc8ee82", + "fact_row_count": 138847, + "facts_sha256": "45bda3ae730d4ae3fa059d9e03304e902f7f6e74c5099355ef937625ca03b72b", + "manifest_sha256": "33a3031523e2cea2f0092a547b97065efbe103cdf85f842142e847b921bcdd9d" } diff --git a/packages/microcosm-build/src/microcosm/build/uk/target_reference_membership.json b/packages/microcosm-build/src/microcosm/build/uk/target_reference_membership.json index 43a7de252..e49ba5496 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/target_reference_membership.json +++ b/packages/microcosm-build/src/microcosm/build/uk/target_reference_membership.json @@ -1,5 +1,5 @@ { - "source_fact_feed": "chronicle-uk-ec7169b5-consumer-facts", + "source_fact_feed": "chronicle-uk-c6f93614-consumer-facts", "target_period": 2025, "candidate_count": 430, "contract_target_count": 202, diff --git a/packages/microcosm-build/tests/test_uk_calibration_seam_driver.py b/packages/microcosm-build/tests/test_uk_calibration_seam_driver.py index 56c5ed744..94d602e16 100644 --- a/packages/microcosm-build/tests/test_uk_calibration_seam_driver.py +++ b/packages/microcosm-build/tests/test_uk_calibration_seam_driver.py @@ -246,13 +246,14 @@ def test_driver_accepts_the_merged_national_feed_without_local_promotion(): driver = _load_driver_module() driver._check_committed_ledger_feed_pin( - "4a50ee9568a01bbb57f73d927084ed6b4b9e52249b51a2338455874ae6e382b5", - manifest_sha256="a95d0ee9f87f36947eaecdb3de29cf81a91e47ccaa822fed42da677eedca877f", + "45bda3ae730d4ae3fa059d9e03304e902f7f6e74c5099355ef937625ca03b72b", + manifest_sha256="33a3031523e2cea2f0092a547b97065efbe103cdf85f842142e847b921bcdd9d", allow_unpinned_feed=False, ) # The local pin is its own reviewed declaration: microcosm#887 moved it to - # the same chronicle ec7169b artifact after a separate local re-pin review, - # so the national acceptance above neither reads nor promotes it. + # the chronicle ec7169b artifact after a separate local re-pin review, and + # the microcosm#890 national re-pin to c6f9361 (chronicle #255 + #258 + # packages) neither reads nor promotes it. assert _LEDGER_FACT_FEED_PIN["facts_sha256"] == ( "4a50ee9568a01bbb57f73d927084ed6b4b9e52249b51a2338455874ae6e382b5" ) @@ -267,7 +268,7 @@ def test_driver_refuses_unpinned_national_manifest(manifest_sha256): driver = _load_driver_module() with pytest.raises(SystemExit, match="manifest"): driver._check_committed_ledger_feed_pin( - "4a50ee9568a01bbb57f73d927084ed6b4b9e52249b51a2338455874ae6e382b5", + "45bda3ae730d4ae3fa059d9e03304e902f7f6e74c5099355ef937625ca03b72b", manifest_sha256=manifest_sha256, allow_unpinned_feed=False, ) diff --git a/packages/microcosm-build/tests/test_uk_national_chronicle_feed.py b/packages/microcosm-build/tests/test_uk_national_chronicle_feed.py index 8b4935162..dc2533d76 100644 --- a/packages/microcosm-build/tests/test_uk_national_chronicle_feed.py +++ b/packages/microcosm-build/tests/test_uk_national_chronicle_feed.py @@ -15,18 +15,18 @@ def test_national_feed_records_the_complete_merged_source_artifact(): pin = load_uk_national_chronicle_feed() resource = files("microcosm.build.uk").joinpath("national_chronicle_feed.json") raw = resource.read_bytes() - assert pin.source_commit == "ec7169b5db40b9f54117c80f70f14efc1dd0fedd" + assert pin.source_commit == "c6f9361492056b9fab7b8535a2be77eb2b6c93bb" assert pin.source_repo == "PolicyEngine/chronicle" - assert pin.fact_row_count == 131450 + assert pin.fact_row_count == 138847 assert pin.facts_sha256 == ( - "4a50ee9568a01bbb57f73d927084ed6b4b9e52249b51a2338455874ae6e382b5" + "45bda3ae730d4ae3fa059d9e03304e902f7f6e74c5099355ef937625ca03b72b" ) assert pin.manifest_sha256 == ( - "a95d0ee9f87f36947eaecdb3de29cf81a91e47ccaa822fed42da677eedca877f" + "33a3031523e2cea2f0092a547b97065efbe103cdf85f842142e847b921bcdd9d" ) assert pin.artifact_schema_version == "policyengine_ledger.consumer_artifact.v2" assert pin.consumer_fact_schema_sha256 == ( - "76ac268e626c86146cee51193e0cbecbb197ddbf3bf410156fe7da7c0edae3ad" + "72ad3149564f8aab3e9bb6de5ea25950c8e19a3e8402e2cdcfc52d250bc8ee82" ) assert pin.resource_sha256 == hashlib.sha256(raw).hexdigest() assert pin.resource_size_bytes == len(raw) diff --git a/packages/microcosm-build/tests/test_uk_target_references.py b/packages/microcosm-build/tests/test_uk_target_references.py index de888534b..6ef3a3c6c 100644 --- a/packages/microcosm-build/tests/test_uk_target_references.py +++ b/packages/microcosm-build/tests/test_uk_target_references.py @@ -84,6 +84,7 @@ def test_local_generator_support_floor_scope_comes_from_signed_register() -> Non ) STABLE_UK_FACT_FEED_NAME = ".codex-work/consumer_facts_uk.jsonl" +STABLE_UK_LOCAL_FACT_FEED_NAME = ".codex-work/consumer_facts_uk_local.jsonl" def _load_uk_resource(name: str) -> dict: @@ -102,19 +103,30 @@ def test_committed_surfaces_regenerate_from_pinned_feed( ) -> None: root = Path(__file__).resolve().parents[3] # Local references keep their independently reviewed historical input. - # Never fall back to the configured national feed for the local surface. + # Never fall back to the configured national feed for the local surface: + # the two surfaces carry independently reviewed pins, so each has its own + # default file under .codex-work. environment_key = ( "CHRONICLE_UK_FACTS" if surface == "national" else "CHRONICLE_UK_LOCAL_FACTS" ) configured = os.environ.get(environment_key) - feed = Path(configured) if configured else root / STABLE_UK_FACT_FEED_NAME + stable_name = ( + STABLE_UK_FACT_FEED_NAME + if surface == "national" + else STABLE_UK_LOCAL_FACT_FEED_NAME + ) + feed = Path(configured) if configured else root / stable_name if not feed.exists(): pytest.skip(f"pinned UK Chronicle {surface} consumer feed is not present") if feed.is_dir(): artifact_path = feed else: - default_manifest = root / ".codex-work/consumer_facts_uk_manifest.json" + default_manifest = root / ( + ".codex-work/consumer_facts_uk_manifest.json" + if surface == "national" + else ".codex-work/consumer_facts_uk_local_manifest.json" + ) manifest = ( default_manifest if not configured else feed.with_name("manifest.json") ) From d6c09151678a714368a21f9c13a6c74ff97a087d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mar=C3=ADa=20Juaristi?= <127882282+juaristi22@users.noreply.github.com> Date: Fri, 11 Sep 2026 12:42:47 +0200 Subject: [PATCH 02/10] Vendor pinned Chronicle facts into UK build resources (#890) Spine stages and target diagnostics need publisher values without reading the feed at build time. tools/vendor_uk_ledger_facts.py copies the facts named by uk/ledger_fact_vendor_selections.json into nine per-concern resources (DfT bus receipts, support, fares index and NTS quintile trips; HMRC clearances, DESNZ prices, OBR receipts by vehicle category and ONS population; DfT licensed cars by fuel type; NEED consumption; Ofgem cap levels and benchmark consumption for FY2024-25; NTS bus-use frequency; devolved bus finance; ORR rail finance; ONS COICOP expenditure). Every row carries the fact keys, source digest and value verbatim plus the national feed identity it was taken from; selectors use the reference-selector vocabulary and are matched by the same function, so an unknown field or a changed match count refuses. A test regenerates each resource byte-identical from the pinned feed. This retires the hand-copied chronicle_candidate anchors one concern at a time. Co-Authored-By: Claude Fable 5.1 --- .../src/microcosm/build/ledger_targets.py | 18 + .../microcosm/build/uk/country_package.json | 50 + .../build/uk/devolved_bus_finance.json | 3982 +++++ .../build/uk/dft_bus_value_anchors.json | 3608 +++++ .../uk/ledger_fact_vendor_selections.json | 380 + .../build/uk/licensed_cars_fuel_type.json | 1707 ++ .../microcosm/build/uk/need_energy_facts.json | 10530 ++++++++++++ .../build/uk/nts_bus_use_frequency.json | 2565 +++ .../build/uk/ofgem_price_cap_facts.json | 13165 ++++++++++++++++ .../uk/ons_household_expenditure_facts.json | 871 + .../microcosm/build/uk/orr_rail_facts.json | 4476 ++++++ .../microcosm/build/uk/road_fuel_anchors.json | 3138 ++++ .../build/uk_runtime/ledger_fact_vendoring.py | 397 + .../tests/test_country_spec.py | 20 + .../tests/test_spec_engine_country_bundles.py | 2 +- .../tests/test_uk_ledger_fact_vendoring.py | 259 + tools/vendor_uk_ledger_facts.py | 123 + 17 files changed, 45290 insertions(+), 1 deletion(-) create mode 100644 packages/microcosm-build/src/microcosm/build/uk/devolved_bus_finance.json create mode 100644 packages/microcosm-build/src/microcosm/build/uk/dft_bus_value_anchors.json create mode 100644 packages/microcosm-build/src/microcosm/build/uk/ledger_fact_vendor_selections.json create mode 100644 packages/microcosm-build/src/microcosm/build/uk/licensed_cars_fuel_type.json create mode 100644 packages/microcosm-build/src/microcosm/build/uk/need_energy_facts.json create mode 100644 packages/microcosm-build/src/microcosm/build/uk/nts_bus_use_frequency.json create mode 100644 packages/microcosm-build/src/microcosm/build/uk/ofgem_price_cap_facts.json create mode 100644 packages/microcosm-build/src/microcosm/build/uk/ons_household_expenditure_facts.json create mode 100644 packages/microcosm-build/src/microcosm/build/uk/orr_rail_facts.json create mode 100644 packages/microcosm-build/src/microcosm/build/uk/road_fuel_anchors.json create mode 100644 packages/microcosm-build/src/microcosm/build/uk_runtime/ledger_fact_vendoring.py create mode 100644 packages/microcosm-build/tests/test_uk_ledger_fact_vendoring.py create mode 100644 tools/vendor_uk_ledger_facts.py diff --git a/packages/microcosm-build/src/microcosm/build/ledger_targets.py b/packages/microcosm-build/src/microcosm/build/ledger_targets.py index 80edd5da6..262c6a448 100644 --- a/packages/microcosm-build/src/microcosm/build/ledger_targets.py +++ b/packages/microcosm-build/src/microcosm/build/ledger_targets.py @@ -2114,6 +2114,24 @@ def _fact_matches_selector(fact: object, selector: Mapping[str, object]) -> bool return True +def fact_matches_selector(fact: object, selector: Mapping[str, object]) -> bool: + """Public name for :func:`_fact_matches_selector` (used by fact vendoring).""" + + return _fact_matches_selector(fact, selector) + + +def selector_field_is_supported(key: str) -> bool: + """Whether ``key`` is in the closed selector vocabulary.""" + + if key in {"dimensions", "dimension_values"}: + return True + try: + _selector_candidates({}, key) + except ValueError: + return False + return True + + def _selector_candidates(fact: object, key: str) -> tuple[str, ...]: if key == "aggregate_fact_key": return (_str_at(fact, "aggregate_fact_key"),) diff --git a/packages/microcosm-build/src/microcosm/build/uk/country_package.json b/packages/microcosm-build/src/microcosm/build/uk/country_package.json index 6805e32df..4aac20377 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/country_package.json +++ b/packages/microcosm-build/src/microcosm/build/uk/country_package.json @@ -326,6 +326,56 @@ "path": "local_target_reference_membership.json", "kind": "legacy_json", "schema_id": "legacy_json" + }, + { + "path": "ledger_fact_vendor_selections.json", + "kind": "legacy_json", + "schema_id": "legacy_json" + }, + { + "path": "dft_bus_value_anchors.json", + "kind": "legacy_json", + "schema_id": "legacy_json" + }, + { + "path": "road_fuel_anchors.json", + "kind": "legacy_json", + "schema_id": "legacy_json" + }, + { + "path": "licensed_cars_fuel_type.json", + "kind": "legacy_json", + "schema_id": "legacy_json" + }, + { + "path": "need_energy_facts.json", + "kind": "legacy_json", + "schema_id": "legacy_json" + }, + { + "path": "ofgem_price_cap_facts.json", + "kind": "legacy_json", + "schema_id": "legacy_json" + }, + { + "path": "nts_bus_use_frequency.json", + "kind": "legacy_json", + "schema_id": "legacy_json" + }, + { + "path": "devolved_bus_finance.json", + "kind": "legacy_json", + "schema_id": "legacy_json" + }, + { + "path": "orr_rail_facts.json", + "kind": "legacy_json", + "schema_id": "legacy_json" + }, + { + "path": "ons_household_expenditure_facts.json", + "kind": "legacy_json", + "schema_id": "legacy_json" } ] } diff --git a/packages/microcosm-build/src/microcosm/build/uk/devolved_bus_finance.json b/packages/microcosm-build/src/microcosm/build/uk/devolved_bus_finance.json new file mode 100644 index 000000000..91a596216 --- /dev/null +++ b/packages/microcosm-build/src/microcosm/build/uk/devolved_bus_finance.json @@ -0,0 +1,3982 @@ +{ + "schema_version": 1, + "country": "uk", + "kind": "uk_vendored_ledger_facts", + "resource": "devolved_bus_finance.json", + "purpose": "Scotland bus and coach statistics (2024 and 2025 editions: journeys, passenger revenue, operator revenue, government support components, fare index); Welsh local authority roads and transport revenue outturn public transport lines for the Wales total; Northern Ireland Transport Holding Company transactions with DfI; DfI Translink bus journeys and passenger receipts by service. Consumed by the devolved cells of the bus-fare and bus-support rakes and the country-leg bus targets.", + "consumers": [ + "lcfs_consumption", + "etb_services", + "uk_runtime.ledger_targets" + ], + "policy": "Rows are copied verbatim from the pinned Chronicle consumer feed named in source_fact_feed; every value is a publisher-stated fact. Consumers declare their own arithmetic on these rows and never edit this file by hand: regenerate it with tools/vendor_uk_ledger_facts.py.", + "generator": { + "tool": "tools/vendor_uk_ledger_facts.py", + "version": 1 + }, + "source_fact_feed": { + "source_repo": "PolicyEngine/chronicle", + "source_commit": "c6f9361492056b9fab7b8535a2be77eb2b6c93bb", + "build": "build-bundle --suite uk -> build-consumer-artifact", + "artifact_schema_version": "policyengine_ledger.consumer_artifact.v2", + "fact_row_count": 138847, + "facts_sha256": "45bda3ae730d4ae3fa059d9e03304e902f7f6e74c5099355ef937625ca03b72b", + "manifest_sha256": "33a3031523e2cea2f0092a547b97065efbe103cdf85f842142e847b921bcdd9d" + }, + "selections": [ + { + "label": "scotland_bus_and_coach_statistics", + "selector": { + "source_name": "scotgov", + "source_concept": [ + "scotgov.bus.passenger_journeys", + "scotgov.bus.passenger_revenue", + "scotgov.bus.operator_revenue", + "scotgov.bus.government_support", + "scotgov.bus.local_fare_index" + ] + }, + "row_count": 18 + }, + { + "label": "wales_transport_revenue_outturn_wales_total", + "selector": { + "source_name": "welshgov", + "source_concept": [ + "welshgov.local_transport.gross_expenditure", + "welshgov.local_transport.total_income", + "welshgov.local_transport.net_current_expenditure", + "welshgov.local_transport.grants" + ], + "geography_id": "W92000004", + "period_type": "fiscal_year" + }, + "row_count": 36 + }, + { + "label": "nithc_transactions_with_dfi", + "selector": { + "source_name": "nithc", + "period_type": "fiscal_year" + }, + "row_count": 8 + }, + { + "label": "dfi_ni_translink_bus_journeys_and_receipts", + "selector": { + "source_name": "dfi_ni", + "source_concept": [ + "dfi_ni.translink_bus.passenger_journeys", + "dfi_ni.translink_bus.passenger_receipts" + ], + "period_type": "fiscal_year" + }, + "row_count": 24 + } + ], + "row_count": 86, + "rows": [ + { + "aggregate_fact_key": "ledger_aggregate_fact_v2_288b81ee8cbcfdfa95bc22cf", + "semantic_fact_key": "ledger_semantic_fact_v2_df8a2df5fa3eca57cce2b853", + "source_record_id": "dfi_ni.public_transport.figure_3.fy2019.metro_glider.passenger_journeys", + "concept": "dfi_ni.translink_bus.passenger_journeys", + "measure_id": "passenger_journeys", + "unit": "count", + "aggregation": "sum", + "entity": { + "name": "person", + "role": "translink_bus_passenger" + }, + "geography": { + "id": "N92000002", + "level": "country", + "vintage": "current" + }, + "period": { + "type": "fiscal_year", + "value": 2019 + }, + "period_coverage": { + "start_date": "2019-04-01", + "end_date": "2020-03-31", + "basis": "fiscal" + }, + "layout": { + "record_set_id": "dfi_ni.public_transport.figure_3.fy2019", + "groupby_dimension": "dfi_ni.bus_service", + "groupby_value_id": "metro_glider", + "table_record_kind": "detail" + }, + "dimensions": { + "service": "metro_glider" + }, + "source": { + "source_name": "dfi_ni", + "source_table": 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Consumed by the LCFS and ETB bus-fare and bus-support rakes and by the BUS0415 period-alignment pass.", + "consumers": [ + "lcfs_consumption", + "etb_services", + "uk_runtime.ledger_targets" + ], + "selections": [ + { + "label": "bus05ai_fare_receipts", + "selector": { + "source_name": "dft", + "source_concept": "dft.local_bus_passenger_fare_receipts", + "period_type": "fiscal_year", + "geography_id": [ + "E92000001", + "E12000007", + "dft:england_outside_london" + ] + }, + "expected_row_count": 9 + }, + { + "label": "bus05bi_net_support", + "selector": { + "source_name": "dft", + "source_concept": "dft.local_bus_total_estimated_net_support", + "period_type": "fiscal_year", + "geography_id": [ + "E92000001", + "E12000007", + "dft:england_outside_london" + ] + }, + "expected_row_count": 9 + }, + { + "label": "bus0415_fares_index", + "selector": { + "source_name": "dft", + "source_concept": "dft.local_bus_fares_index", + "period_type": "month", + "geography_id": [ + "E92000001", + "E12000007", + "dft:england_outside_london" + ] + }, + "expected_row_count": 39 + }, + { + "label": "nts0705a_local_bus_trips_by_income_quintile", + "selector": { + "source_name": "dft", + "source_concept": [ + "dft.other_local_bus_trips_per_person", + "dft.bus_in_london_trips_per_person" + ], + "period_type": "calendar_year" + }, + "expected_row_count": 24 + } + ] + }, + { + "resource": "road_fuel_anchors.json", + "purpose": "HMRC Hydrocarbon Oils Bulletin petrol and diesel quantities released for consumption by fiscal year; DESNZ annual average ULSP and ULSD pump prices; OBR fuel duty receipts by vehicle category; ONS mid-year UK population totals. Consumed by the LCFS donor litre-proxy uprating and the household-incidence fuel diagnostics.", + "consumers": [ + "lcfs_consumption", + "uk_aggregate_admin" + ], + "selections": [ + { + "label": "hmrc_road_fuel_quantities_fiscal_year", + "selector": { + "source_name": "hmrc", + "source_concept": [ + "hmrc.hydrocarbon_oils.total_petrol_quantity", + "hmrc.hydrocarbon_oils.total_diesel_quantity" + ], + "period_type": "fiscal_year" + }, + "expected_row_count": 12 + }, + { + "label": "desnz_annual_pump_prices", + "selector": { + "source_name": "desnz", + "source_concept": [ + "desnz.road_fuel.annual_ulsp_pump_price", + "desnz.road_fuel.annual_ulsd_pump_price" + ], + "period_type": "calendar_year" + }, + "expected_row_count": 6 + }, + { + "label": "obr_fuel_duty_receipts_by_vehicle_category", + "selector": { + "source_name": "obr", + "source_concept": "obr.fuel_duty.receipts", + "period_type": "fiscal_year" + }, + "expected_row_count": 49 + }, + { + "label": "ons_mid_year_population_uk_total", + "selector": { + "source_name": "ons", + "source_concept": "ons.mid_year_population_estimate", + "geography_id": "K02000001", + "dimensions": [] + }, + "expected_row_count": 2 + } + ] + }, + { + "resource": "licensed_cars_fuel_type.json", + "purpose": "DfT VEH1103a licensed cars by fuel type, Great Britain, end-year stock 2023 to 2025. Consumed by the LCFS stage's ICE share in place of the nts_ice_share anchor.", + "consumers": [ + "lcfs_consumption" + ], + "selections": [ + { + "label": "veh1103_licensed_cars_by_fuel_type", + "selector": { + "source_name": "dft", + "source_concept": [ + "dft.licensed_cars.all", + "dft.licensed_cars.petrol", + "dft.licensed_cars.diesel", + "dft.licensed_cars.battery_electric", + "dft.licensed_cars.plug_in_hybrid_petrol", + "dft.licensed_cars.plug_in_hybrid_diesel", + "dft.licensed_cars.hybrid_electric_petrol", + "dft.licensed_cars.hybrid_electric_diesel", + "dft.licensed_cars.range_extended_electric", + "dft.licensed_cars.fuel_cell_electric", + "dft.licensed_cars.gas", + "dft.licensed_cars.other" + ], + "period_type": "calendar_year" + }, + "expected_row_count": 36 + } + ] + }, + { + "resource": "need_energy_facts.json", + "purpose": "DESNZ NEED 2023 mean and median household gas and electricity consumption (kWh) by property type, tenure, household income band and region for England and Wales, and by property type, tenure and income band for Scotland. Consumed by the LCFS energy rake in place of need_energy_targets.json.", + "consumers": [ + "lcfs_consumption", + "uk_aggregate_admin" + ], + "selections": [ + { + "label": "need_household_energy_consumption", + "selector": { + "source_name": "desnz", + "source_concept": [ + "desnz.need.household_gas_consumption", + "desnz.need.household_electricity_consumption" + ], + "period_type": "calendar_year" + }, + "expected_row_count": 228 + } + ] + }, + { + "resource": "ofgem_price_cap_facts.json", + "purpose": "Ofgem default tariff cap levels at nil and benchmark consumption for the four charge restriction periods of FY2024-25 (2024-Q2 to 2025-Q1), direct debit, single-rate electricity and gas, every charge restriction region excluding VAT plus the GB average including and excluding VAT; and the benchmark consumption each level is set at. Consumed by the LCFS energy pricing (unit rate = (benchmark - nil) / benchmark consumption; nil = annual standing charge; VAT added from the engine parameter).", + "consumers": [ + "lcfs_consumption", + "uk_aggregate_admin" + ], + "selections": [ + { + "label": "ofgem_cap_levels_fy2024_25_direct_debit", + "selector": { + "source_name": "ofgem", + "source_concept": "ofgem.price_cap.cap_level", + "period_type": "quarter", + "period_value": [ + "2024-Q2", + "2024-Q3", + "2024-Q4", + "2025-Q1" + ], + "dimension_values": { + "payment_method": [ + "direct_debit" + ], + "fuel": [ + "electricity_single_rate", + "gas" + ] + } + }, + "expected_row_count": 256 + }, + { + "label": "ofgem_benchmark_consumption_fy2024_25", + "selector": { + "source_name": "ofgem", + "source_concept": "ofgem.price_cap.benchmark_consumption", + "period_type": "quarter", + "period_value": [ + "2024-Q2", + "2024-Q3", + "2024-Q4", + "2025-Q1" + ] + }, + "expected_row_count": 12 + } + ] + }, + { + "resource": "nts_bus_use_frequency.json", + "purpose": "DfT NTS0313 shares of people by frequency of local bus use (all ages, England) and NTS0621 the same bands for people aged 60 and over, 2023 to 2025, with the unweighted sample sizes. 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Consumed by the devolved cells of the bus-fare and bus-support rakes and the country-leg bus targets.", + "consumers": [ + "lcfs_consumption", + "etb_services", + "uk_runtime.ledger_targets" + ], + "selections": [ + { + "label": "scotland_bus_and_coach_statistics", + "selector": { + "source_name": "scotgov", + "source_concept": [ + "scotgov.bus.passenger_journeys", + "scotgov.bus.passenger_revenue", + "scotgov.bus.operator_revenue", + "scotgov.bus.government_support", + "scotgov.bus.local_fare_index" + ] + }, + "expected_row_count": 18 + }, + { + "label": "wales_transport_revenue_outturn_wales_total", + "selector": { + "source_name": "welshgov", + "source_concept": [ + "welshgov.local_transport.gross_expenditure", + "welshgov.local_transport.total_income", + "welshgov.local_transport.net_current_expenditure", + "welshgov.local_transport.grants" + ], + "geography_id": "W92000004", + "period_type": "fiscal_year" + }, + "expected_row_count": 36 + }, + { + "label": "nithc_transactions_with_dfi", + "selector": { + "source_name": "nithc", + "period_type": "fiscal_year" + }, + "expected_row_count": 8 + }, + { + "label": "dfi_ni_translink_bus_journeys_and_receipts", + "selector": { + "source_name": "dfi_ni", + "source_concept": [ + "dfi_ni.translink_bus.passenger_journeys", + "dfi_ni.translink_bus.passenger_receipts" + ], + "period_type": "fiscal_year" + }, + "expected_row_count": 24 + } + ] + }, + { + "resource": "orr_rail_facts.json", + "purpose": "ORR rail industry finance: total government support to the rail industry (table 7270), government support by funding source (7271), franchised operator fare income (7223) and the rail fares index (7180). Consumed by the rail support target diagnostics and the fare-index lockstep check.", + "consumers": [ + "uk_runtime.ledger_targets", + "uk_aggregate_admin" + ], + "selections": [ + { + "label": "orr_rail_industry_finance", + "selector": { + "source_name": "orr", + "source_concept": [ + "orr.rail.total_government_support", + "orr.rail.government_support_by_source", + "orr.rail.franchised_operator_fare_income", + "orr.rail.fare_index" + ] + }, + "expected_row_count": 99 + } + ] + }, + { + "resource": "ons_household_expenditure_facts.json", + "purpose": "ONS Consumer Trends household final consumption expenditure, current prices, annual, for COICOP 04.5 electricity, gas and other fuels, 07.2.2 fuels and lubricants for personal transport and 07.3.2 passenger transport by road, 2020 to 2025. Consumed by the domestic energy spend target and the motor-fuel spend diagnostic.", + "consumers": [ + "uk_runtime.ledger_targets", + "uk_aggregate_admin" + ], + "selections": [ + { + "label": "ons_consumer_trends_annual_coicop", + "selector": { + "source_name": "ons", + "source_concept": [ + "ons.household_expenditure.electricity_gas_other_fuels", + "ons.household_expenditure.personal_transport_fuels_lubricants", + "ons.household_expenditure.road_transport_services" + ], + "period_type": "calendar_year" + }, + "expected_row_count": 18 + } + ] + } + ] +} diff --git a/packages/microcosm-build/src/microcosm/build/uk/licensed_cars_fuel_type.json b/packages/microcosm-build/src/microcosm/build/uk/licensed_cars_fuel_type.json new file mode 100644 index 000000000..e4961d534 --- /dev/null +++ b/packages/microcosm-build/src/microcosm/build/uk/licensed_cars_fuel_type.json @@ -0,0 +1,1707 @@ +{ + "schema_version": 1, + "country": "uk", + "kind": "uk_vendored_ledger_facts", + "resource": "licensed_cars_fuel_type.json", + "purpose": "DfT VEH1103a licensed cars by fuel type, Great Britain, end-year stock 2023 to 2025. Consumed by the LCFS stage's ICE share in place of the nts_ice_share anchor.", + "consumers": [ + "lcfs_consumption" + ], + "policy": "Rows are copied verbatim from the pinned Chronicle consumer feed named in source_fact_feed; every value is a publisher-stated fact. Consumers declare their own arithmetic on these rows and never edit this file by hand: regenerate it with tools/vendor_uk_ledger_facts.py.", + "generator": { + "tool": "tools/vendor_uk_ledger_facts.py", + "version": 1 + }, + "source_fact_feed": { + "source_repo": "PolicyEngine/chronicle", + "source_commit": "c6f9361492056b9fab7b8535a2be77eb2b6c93bb", + "build": "build-bundle --suite uk -> build-consumer-artifact", + "artifact_schema_version": "policyengine_ledger.consumer_artifact.v2", + "fact_row_count": 138847, + "facts_sha256": "45bda3ae730d4ae3fa059d9e03304e902f7f6e74c5099355ef937625ca03b72b", + "manifest_sha256": "33a3031523e2cea2f0092a547b97065efbe103cdf85f842142e847b921bcdd9d" + }, + "selections": [ + { + "label": "veh1103_licensed_cars_by_fuel_type", + "selector": { + "source_name": "dft", + "source_concept": [ + "dft.licensed_cars.all", + "dft.licensed_cars.petrol", + "dft.licensed_cars.diesel", + "dft.licensed_cars.battery_electric", + 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"groupby_value_id": "other_fuels", + "table_record_kind": "detail" + }, + "dimensions": { + "vehicle_category": "other_fuels" + }, + "source": { + "source_name": "obr", + "source_table": "Fuel duty supplementary release: receipts by vehicle type", + "source_file": "fuel-duty-receipts-by-vehicle-type.pdf", + "source_sha256": "4c5a65745550fbc62fb281c43dbde602a64f465b6095e3383e0473d66b31f018", + "vintage": "april_2024", + "raw_r2_key": "raw/obr/obr-fuel-duty-receipts-by-vehicle-april-2024/2024/4c5a65745550fbc62fb281c43dbde602a64f465b6095e3383e0473d66b31f018/fuel-duty-receipts-by-vehicle-type.pdf" + }, + "value": 300000000 + }, + { + "aggregate_fact_key": "ledger_aggregate_fact_v2_187f17fefbaf56eb34f7ee6a", + "semantic_fact_key": "ledger_semantic_fact_v2_3cf616b89be7bd38e9b8613b", + "source_record_id": "obr.fuel_duty_receipts_by_vehicle.fy2028.other_petrol_diesel.receipts", + "concept": "obr.fuel_duty.receipts", + "measure_id": "receipts", + "unit": "gbp", + "aggregation": "sum", + "entity": { + "name": "government", + "role": "central_government" + }, + "geography": { + "id": "K02000001", + "level": "country", + "vintage": "current" + }, + "period": { + "type": "fiscal_year", + "value": 2028 + }, + "period_coverage": { + "start_date": "2028-04-01", + "end_date": "2029-03-31", + "basis": "fiscal" + }, + "layout": { + "record_set_id": "obr.fuel_duty_receipts_by_vehicle.fy2028", + "groupby_dimension": "obr.vehicle_category", + "groupby_value_id": "other_petrol_diesel", + "table_record_kind": "detail" + }, + "dimensions": { + "vehicle_category": "other_petrol_diesel" + }, + "source": { + "source_name": "obr", + "source_table": "Fuel duty supplementary release: receipts by vehicle type", + "source_file": "fuel-duty-receipts-by-vehicle-type.pdf", + "source_sha256": "4c5a65745550fbc62fb281c43dbde602a64f465b6095e3383e0473d66b31f018", + "vintage": "april_2024", + "raw_r2_key": "raw/obr/obr-fuel-duty-receipts-by-vehicle-april-2024/2024/4c5a65745550fbc62fb281c43dbde602a64f465b6095e3383e0473d66b31f018/fuel-duty-receipts-by-vehicle-type.pdf" + }, + "value": 1400000000 + }, + { + "aggregate_fact_key": "ledger_aggregate_fact_v2_321c40f8a85036feaf0031dd", + "semantic_fact_key": "ledger_semantic_fact_v2_c7fdfcb914a922459d2d5496", + "source_record_id": "obr.fuel_duty_receipts_by_vehicle.fy2028.road_fuels.receipts", + "concept": "obr.fuel_duty.receipts", + "measure_id": "receipts", + "unit": "gbp", + "aggregation": "sum", + "entity": { + "name": "government", + "role": "central_government" + }, + "geography": { + "id": "K02000001", + "level": "country", + "vintage": "current" + }, + "period": { + "type": "fiscal_year", + "value": 2028 + }, + "period_coverage": { + "start_date": "2028-04-01", + "end_date": "2029-03-31", + "basis": "fiscal" + }, + "layout": { + "record_set_id": "obr.fuel_duty_receipts_by_vehicle.fy2028", + "groupby_dimension": "obr.vehicle_category", + "groupby_value_id": "road_fuels", + "table_record_kind": "detail" + }, + "dimensions": { + "vehicle_category": "road_fuels" + }, + "source": { + "source_name": "obr", + "source_table": "Fuel duty supplementary release: receipts by vehicle type", + "source_file": "fuel-duty-receipts-by-vehicle-type.pdf", + "source_sha256": "4c5a65745550fbc62fb281c43dbde602a64f465b6095e3383e0473d66b31f018", + "vintage": "april_2024", + "raw_r2_key": "raw/obr/obr-fuel-duty-receipts-by-vehicle-april-2024/2024/4c5a65745550fbc62fb281c43dbde602a64f465b6095e3383e0473d66b31f018/fuel-duty-receipts-by-vehicle-type.pdf" + }, + "value": 27900000000 + }, + { + "aggregate_fact_key": "ledger_aggregate_fact_v2_7a5a32cf303e35399bc3a18b", + "semantic_fact_key": "ledger_semantic_fact_v2_81b71afa26a658c7061b6a70", + "source_record_id": "obr.fuel_duty_receipts_by_vehicle.fy2028.total.receipts", + "concept": "obr.fuel_duty.receipts", + "measure_id": "receipts", + "unit": "gbp", + "aggregation": "sum", + "entity": { + "name": "government", + "role": "central_government" + }, + "geography": { + "id": "K02000001", + "level": "country", + "vintage": "current" + }, + "period": { + "type": "fiscal_year", + "value": 2028 + }, + "period_coverage": { + "start_date": "2028-04-01", + "end_date": "2029-03-31", + "basis": "fiscal" + }, + "layout": { + "record_set_id": "obr.fuel_duty_receipts_by_vehicle.fy2028", + "groupby_dimension": "obr.vehicle_category", + "groupby_value_id": "total", + "table_record_kind": "total" + }, + "dimensions": { + "vehicle_category": "total" + }, + "source": { + "source_name": "obr", + "source_table": "Fuel duty supplementary release: receipts by vehicle type", + "source_file": "fuel-duty-receipts-by-vehicle-type.pdf", + "source_sha256": "4c5a65745550fbc62fb281c43dbde602a64f465b6095e3383e0473d66b31f018", + "vintage": "april_2024", + "raw_r2_key": "raw/obr/obr-fuel-duty-receipts-by-vehicle-april-2024/2024/4c5a65745550fbc62fb281c43dbde602a64f465b6095e3383e0473d66b31f018/fuel-duty-receipts-by-vehicle-type.pdf" + }, + "value": 28200000000 + }, + { + "aggregate_fact_key": "ledger_aggregate_fact_v2_ed134450bebb0e771ad09459", + "semantic_fact_key": "ledger_semantic_fact_v2_ea26a31f63f4803bfc8ed989", + "source_record_id": "ons.mid2023.mye2_persons.uk_countries.all_ages.uk.population", + "concept": "ons.mid_year_population_estimate", + "measure_id": "population", + "unit": "count", + "aggregation": "sum", + "entity": { + "name": "person", + "role": "resident_population" + }, + "geography": { + "id": "K02000001", + "level": "country", + "vintage": "gss_2023" + }, + "period": { + "type": "calendar_year", + "value": 2023 + }, + "period_coverage": { + "start_date": null, + "end_date": null, + "basis": null + }, + "layout": { + "record_set_id": "ons.mid2023.mye2_persons.uk_countries.all_ages", + "groupby_dimension": "age", + "groupby_value_id": "uk", + "table_record_kind": "total" + }, + "dimensions": {}, + "source": { + "source_name": "ons", + "source_table": "Mid-2023 population estimates, UK and constituent countries, by single year of age and sex (MYE2)", + "source_file": "mye23tablesuk.xlsx", + "source_sha256": "7ee5724ecf71abc3d811c89174cdddf1bce7f07d3eefc8b69b15ee8371414b16", + "vintage": "mid_2023", + "raw_r2_key": "raw/ons/ons-mye-2023-uk-countries/2023/7ee5724ecf71abc3d811c89174cdddf1bce7f07d3eefc8b69b15ee8371414b16/mye23tablesuk.xlsx" + }, + "value": 68265209 + }, + { + "aggregate_fact_key": "ledger_aggregate_fact_v2_6c212695ea75be0d2a635e9b", + "semantic_fact_key": "ledger_semantic_fact_v2_065ea5242b0c7b9f810a9ce6", + "source_record_id": "ons.mid2024.mye2_persons.uk.all_ages.uk.population", + "concept": "ons.mid_year_population_estimate", + "measure_id": "population", + "unit": "count", + "aggregation": "sum", + "entity": { + "name": "person", + "role": "resident_population" + }, + "geography": { + "id": "K02000001", + "level": "country", + "vintage": "gss_2024" + }, + "period": { + "type": "calendar_year", + "value": 2024 + }, + "period_coverage": { + "start_date": null, + "end_date": null, + "basis": null + }, + "layout": { + "record_set_id": "ons.mid2024.mye2_persons.uk.all_ages", + "groupby_dimension": "age", + "groupby_value_id": "uk", + "table_record_kind": "total" + }, + "dimensions": {}, + "source": { + "source_name": "ons", + "source_table": "Mid-2024 population estimates, UK: countries and regions by single year of age and sex (MYE2)", + "source_file": "mye24tablesuk.xlsx", + "source_sha256": "f78d50e8d77fe5ed9d04667d6875aefcf62b56380af7aceb2e90dce067981526", + "vintage": "mid_2024", + "raw_r2_key": "raw/ons/ons-mye-2024-uk/2024/f78d50e8d77fe5ed9d04667d6875aefcf62b56380af7aceb2e90dce067981526/mye24tablesuk.xlsx" + }, + "value": 69281437 + } + ] +} diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/ledger_fact_vendoring.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/ledger_fact_vendoring.py new file mode 100644 index 000000000..4472b2dbc --- /dev/null +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/ledger_fact_vendoring.py @@ -0,0 +1,397 @@ +"""Vendor hash-pinned Chronicle facts as UK build resources. + +Spine stages need publisher values (DfT bus receipts, NTS trips, Ofgem cap +levels, NEED consumption, HMRC clearances, DESNZ prices) without reading the +Chronicle feed at build time. This module copies the exact consumer facts a +committed selection register names into per-concern JSON resources, carrying +each fact's keys, source digest and value verbatim and the identity of the +pinned feed they were taken from. Nothing here derives, uprates, scales or +totals a value: the stage that consumes a resource declares its own arithmetic. + +The selection register (``ledger_fact_vendor_selections.json``) uses the same +closed selector vocabulary as the target references, matched by the same +function, so a selection reads like a reference selector and fails the same +way on an unknown field. +""" + +from __future__ import annotations + +import hashlib +import json +from collections.abc import Iterable, Mapping, Sequence +from dataclasses import dataclass +from importlib.resources import files +from typing import Any + +from microcosm.build.ledger_targets import ( + fact_matches_selector, + selector_field_is_supported, +) +from microcosm.build.target_reference_authoring import _json_safe_ledger_id +from microcosm.build.uk_runtime.national_chronicle_feed import ( + UKNationalChronicleFeed, +) + +VENDOR_SELECTIONS_RESOURCE = "ledger_fact_vendor_selections.json" +VENDOR_SELECTIONS_KIND = "uk_ledger_fact_vendor_selections" +VENDORED_RESOURCE_KIND = "uk_vendored_ledger_facts" +VENDOR_TOOL = "tools/vendor_uk_ledger_facts.py" +VENDOR_TOOL_VERSION = 1 +_SELECTION_KEYS = frozenset({"label", "selector", "expected_row_count"}) +_RESOURCE_KEYS = frozenset({"resource", "purpose", "consumers", "selections"}) +_ROW_ORDER_KEYS = ( + "concept", + "geography_id", + "period_type", + "period_value", + "groupby_value_id", + "dimensions", + "measure_id", + "aggregate_fact_key", +) + + +@dataclass(frozen=True) +class VendoredResource: + """One generated resource and the bytes it serialises to.""" + + resource: str + payload: dict[str, Any] + + @property + def content(self) -> bytes: + return (json.dumps(self.payload, indent=2) + "\n").encode("utf-8") + + @property + def sha256(self) -> str: + return hashlib.sha256(self.content).hexdigest() + + @property + def row_count(self) -> int: + return len(self.payload["rows"]) + + +def load_vendor_selections() -> dict[str, Any]: + """Load the committed selection register from the UK country package.""" + + register = json.loads( + files("microcosm.build.uk") + .joinpath(VENDOR_SELECTIONS_RESOURCE) + .read_text(encoding="utf-8") + ) + validate_vendor_selections(register) + return register + + +def validate_vendor_selections(register: Mapping[str, Any]) -> None: + """Refuse a register whose shape or selector vocabulary is not closed.""" + + if register.get("schema_version") != 1: + raise ValueError("Vendor selection register schema_version must be 1.") + if register.get("country") != "uk": + raise ValueError("Vendor selection register country must be 'uk'.") + if register.get("kind") != VENDOR_SELECTIONS_KIND: + raise ValueError( + f"Vendor selection register kind must be {VENDOR_SELECTIONS_KIND!r}." + ) + resources = register.get("resources") + if not isinstance(resources, list) or not resources: + raise ValueError("Vendor selection register must list at least one resource.") + seen: set[str] = set() + for entry in resources: + if not isinstance(entry, Mapping) or set(entry) != _RESOURCE_KEYS: + raise ValueError( + "Each vendor selection resource must carry exactly " + f"{sorted(_RESOURCE_KEYS)}; got {sorted(entry) if isinstance(entry, Mapping) else entry!r}." + ) + name = entry["resource"] + if not isinstance(name, str) or not name.endswith(".json") or "/" in name: + raise ValueError( + f"Vendored resource name {name!r} must be a bare .json filename." + ) + if name in seen: + raise ValueError(f"Vendored resource {name!r} is declared twice.") + seen.add(name) + if not isinstance(entry["purpose"], str) or not entry["purpose"].strip(): + raise ValueError(f"Vendored resource {name!r} needs a non-blank purpose.") + consumers = entry["consumers"] + if not isinstance(consumers, list) or not all( + isinstance(item, str) and item for item in consumers + ): + raise ValueError( + f"Vendored resource {name!r} consumers must be a list of names." + ) + selections = entry["selections"] + if not isinstance(selections, list) or not selections: + raise ValueError( + f"Vendored resource {name!r} needs at least one selection." + ) + labels: set[str] = set() + for selection in selections: + if not isinstance(selection, Mapping) or set(selection) != _SELECTION_KEYS: + raise ValueError( + f"Vendored resource {name!r}: each selection must carry exactly " + f"{sorted(_SELECTION_KEYS)}." + ) + label = selection["label"] + if not isinstance(label, str) or not label.strip() or label in labels: + raise ValueError( + f"Vendored resource {name!r}: selection labels must be unique, non-blank strings." + ) + labels.add(label) + selector = selection["selector"] + if not isinstance(selector, Mapping) or not selector: + raise ValueError( + f"Vendored resource {name!r} selection {label!r}: selector must be a non-empty mapping." + ) + for field in selector: + if not selector_field_is_supported(str(field)): + raise ValueError( + f"Vendored resource {name!r} selection {label!r}: " + f"unsupported selector field {field!r}." + ) + count = selection["expected_row_count"] + if type(count) is not int or count <= 0: + raise ValueError( + f"Vendored resource {name!r} selection {label!r}: " + "expected_row_count must be a positive integer." + ) + + +def feed_identity(pin: UKNationalChronicleFeed) -> dict[str, Any]: + """The pinned feed identity every vendored resource records.""" + + return { + "source_repo": pin.source_repo, + "source_commit": pin.source_commit, + "build": pin.build, + "artifact_schema_version": pin.artifact_schema_version, + "fact_row_count": pin.fact_row_count, + "facts_sha256": pin.facts_sha256, + "manifest_sha256": pin.manifest_sha256, + } + + +def vendor_row(fact: Mapping[str, Any]) -> dict[str, Any]: + """Copy the identifying keys, provenance and value of one consumer fact.""" + + observed = fact.get("observed_measure") or {} + source = fact.get("source") or {} + layout = fact.get("layout") or {} + geography = fact.get("geography") or {} + period = fact.get("period") or {} + coverage = fact.get("period_coverage") or {} + entity = fact.get("entity") or {} + lineage = fact.get("lineage") or {} + aggregation = fact.get("aggregation") or {} + dimensions = fact.get("dimensions") or {} + value = fact.get("value") + if not isinstance(value, (int, float)) or isinstance(value, bool): + raise ValueError( + f"Consumer fact {fact.get('aggregate_fact_key')!r} has a non-numeric value." + ) + # Fact keys are stored JSON-safe (dots and colons to underscores), the + # membership register's spelling, so a country-package resource never + # carries a module:attribute-shaped string. + row = { + "aggregate_fact_key": _json_safe_ledger_id( + _required(fact, "aggregate_fact_key") + ), + "semantic_fact_key": _json_safe_ledger_id(_required(fact, "semantic_fact_key")), + "source_record_id": str(lineage.get("source_record_id") or ""), + "concept": str(observed.get("source_concept") or ""), + "measure_id": str(observed.get("source_measure_id") or ""), + "unit": observed.get("unit"), + "aggregation": aggregation.get("method"), + "entity": {"name": entity.get("name"), "role": entity.get("role")}, + "geography": { + "id": geography.get("id"), + "level": geography.get("level"), + "vintage": geography.get("vintage"), + }, + "period": {"type": period.get("type"), "value": period.get("value")}, + "period_coverage": { + "start_date": coverage.get("start_date"), + "end_date": coverage.get("end_date"), + "basis": coverage.get("basis"), + }, + "layout": { + "record_set_id": layout.get("record_set_id"), + "groupby_dimension": layout.get("groupby_dimension"), + "groupby_value_id": layout.get("groupby_value_id"), + "table_record_kind": layout.get("table_record_kind"), + }, + "dimensions": dict(sorted(dimensions.items())), + "source": { + "source_name": source.get("source_name"), + "source_table": source.get("source_table"), + "source_file": source.get("source_file"), + "source_sha256": source.get("source_sha256"), + "vintage": source.get("vintage"), + "raw_r2_key": source.get("raw_r2_key"), + }, + "value": value, + } + if not row["concept"]: + raise ValueError( + f"Consumer fact {row['aggregate_fact_key']!r} carries no source concept." + ) + return row + + +def vendor_resource( + entry: Mapping[str, Any], + facts: Iterable[Mapping[str, Any]], + *, + pin: UKNationalChronicleFeed, + strict_counts: bool = True, +) -> VendoredResource: + """Select and copy the facts one register entry names.""" + + fact_list = list(facts) + selections_out: list[dict[str, Any]] = [] + rows: dict[str, dict[str, Any]] = {} + for selection in entry["selections"]: + selector = dict(selection["selector"]) + matched = [fact for fact in fact_list if fact_matches_selector(fact, selector)] + expected = int(selection["expected_row_count"]) + if strict_counts and len(matched) != expected: + raise ValueError( + f"Vendored resource {entry['resource']!r} selection " + f"{selection['label']!r} matched {len(matched)} facts, " + f"expected {expected}." + ) + for fact in matched: + row = vendor_row(fact) + key = row["aggregate_fact_key"] + previous = rows.get(key) + if previous is not None and previous != row: + raise ValueError( + f"Vendored resource {entry['resource']!r}: aggregate fact " + f"{key!r} matched two selections with different rows." + ) + rows[key] = row + selections_out.append( + { + "label": selection["label"], + "selector": selector, + "row_count": len(matched), + } + ) + ordered = sorted(rows.values(), key=_row_sort_key) + payload = { + "schema_version": 1, + "country": "uk", + "kind": VENDORED_RESOURCE_KIND, + "resource": entry["resource"], + "purpose": entry["purpose"], + "consumers": list(entry["consumers"]), + "policy": ( + "Rows are copied verbatim from the pinned Chronicle consumer feed named " + "in source_fact_feed; every value is a publisher-stated fact. Consumers " + "declare their own arithmetic on these rows and never edit this file by " + f"hand: regenerate it with {VENDOR_TOOL}." + ), + "generator": {"tool": VENDOR_TOOL, "version": VENDOR_TOOL_VERSION}, + "source_fact_feed": feed_identity(pin), + "selections": selections_out, + "row_count": len(ordered), + "rows": ordered, + } + return VendoredResource(resource=entry["resource"], payload=payload) + + +def vendor_all( + register: Mapping[str, Any], + facts: Sequence[Mapping[str, Any]], + *, + pin: UKNationalChronicleFeed, + only: Iterable[str] | None = None, + strict_counts: bool = True, +) -> list[VendoredResource]: + wanted = set(only) if only is not None else None + results = [] + for entry in register["resources"]: + if wanted is not None and entry["resource"] not in wanted: + continue + results.append( + vendor_resource(entry, facts, pin=pin, strict_counts=strict_counts) + ) + if wanted is not None: + missing = wanted - {result.resource for result in results} + if missing: + raise ValueError(f"Unknown vendored resource(s): {sorted(missing)}.") + return results + + +def load_vendored_resource(name: str) -> dict[str, Any]: + """Load a committed vendored resource and check its header shape.""" + + payload = json.loads( + files("microcosm.build.uk").joinpath(name).read_text(encoding="utf-8") + ) + if payload.get("kind") != VENDORED_RESOURCE_KIND or payload.get("resource") != name: + raise ValueError(f"{name} is not a vendored Chronicle facts resource.") + if payload.get("row_count") != len(payload.get("rows", ())): + raise ValueError(f"{name}: row_count disagrees with the rows carried.") + return payload + + +def rows_matching( + payload: Mapping[str, Any], **criteria: object +) -> list[dict[str, Any]]: + """Filter vendored rows by top-level scalar fields or dotted sub-keys. + + ``rows_matching(payload, concept="x", period_type="fiscal_year")`` filters + on ``row["concept"]`` and ``row["period"]["type"]``; ``geography_id`` and + ``period_value`` follow the same spelling as the selector vocabulary. + """ + + aliases = { + "period_type": ("period", "type"), + "period_value": ("period", "value"), + "geography_id": ("geography", "id"), + "geography_level": ("geography", "level"), + "groupby_value_id": ("layout", "groupby_value_id"), + "record_set_id": ("layout", "record_set_id"), + } + result = [] + for row in payload["rows"]: + keep = True + for key, expected in criteria.items(): + path = aliases.get(key, (key,)) + actual: Any = row + for part in path: + actual = actual.get(part) if isinstance(actual, Mapping) else None + if isinstance(expected, (list, tuple, set, frozenset)): + if actual not in expected and str(actual) not in { + str(e) for e in expected + }: + keep = False + break + elif actual != expected and str(actual) != str(expected): + keep = False + break + if keep: + result.append(row) + return result + + +def _row_sort_key(row: Mapping[str, Any]) -> tuple[str, ...]: + return ( + str(row["concept"]), + str(row["geography"]["id"]), + str(row["period"]["type"]), + str(row["period"]["value"]), + str(row["layout"]["groupby_value_id"]), + json.dumps(row["dimensions"], sort_keys=True), + str(row["measure_id"]), + str(row["aggregate_fact_key"]), + ) + + +def _required(fact: Mapping[str, Any], key: str) -> str: + value = fact.get(key) + if not isinstance(value, str) or not value: + raise ValueError(f"Consumer fact is missing {key!r}.") + return value diff --git a/packages/microcosm-build/tests/test_country_spec.py b/packages/microcosm-build/tests/test_country_spec.py index 84eb4e1b1..f33b2a71e 100644 --- a/packages/microcosm-build/tests/test_country_spec.py +++ b/packages/microcosm-build/tests/test_country_spec.py @@ -961,6 +961,16 @@ def test_spi_spine_adds_no_country_package_resources(self) -> None: "target_reference_membership.json", "local_target_references.json", "local_target_reference_membership.json", + "ledger_fact_vendor_selections.json", + "dft_bus_value_anchors.json", + "road_fuel_anchors.json", + "licensed_cars_fuel_type.json", + "need_energy_facts.json", + "ofgem_price_cap_facts.json", + "nts_bus_use_frequency.json", + "devolved_bus_finance.json", + "orr_rail_facts.json", + "ons_household_expenditure_facts.json", ) def test_uk_source_manifest_loads_thirty_stages(self) -> None: @@ -1055,6 +1065,16 @@ def test_uk_package_loads(self) -> None: "target_reference_membership.json", "local_target_references.json", "local_target_reference_membership.json", + "ledger_fact_vendor_selections.json", + "dft_bus_value_anchors.json", + "road_fuel_anchors.json", + "licensed_cars_fuel_type.json", + "need_energy_facts.json", + "ofgem_price_cap_facts.json", + "nts_bus_use_frequency.json", + "devolved_bus_finance.json", + "orr_rail_facts.json", + "ons_household_expenditure_facts.json", ) def test_uk_target_references_accept_regenerated_contract_fields(self) -> None: diff --git a/packages/microcosm-build/tests/test_spec_engine_country_bundles.py b/packages/microcosm-build/tests/test_spec_engine_country_bundles.py index 1169547c3..694126872 100644 --- a/packages/microcosm-build/tests/test_spec_engine_country_bundles.py +++ b/packages/microcosm-build/tests/test_spec_engine_country_bundles.py @@ -55,7 +55,7 @@ ), ( "uk", - "2269bc281ada99dce077730657e3868972e025b48be5f9db8c11b3cc63285bac", + "d458fe14e72ff4d9f28e82af7aa16f2c72795e62d496506ea8aa4445c94d0342", { "benunit.benunit_id", "household.household_id", diff --git a/packages/microcosm-build/tests/test_uk_ledger_fact_vendoring.py b/packages/microcosm-build/tests/test_uk_ledger_fact_vendoring.py new file mode 100644 index 000000000..b2d8d822b --- /dev/null +++ b/packages/microcosm-build/tests/test_uk_ledger_fact_vendoring.py @@ -0,0 +1,259 @@ +"""Vendored Chronicle facts are pinned copies, regenerable from the feed.""" + +from __future__ import annotations + +import hashlib +import json +import os +import subprocess +import sys +from importlib import resources as importlib_resources +from pathlib import Path + +import pytest + +from microcosm.build.ledger_artifact import load_ledger_consumer_artifact +from microcosm.build.uk_runtime.ledger_fact_vendoring import ( + VENDOR_SELECTIONS_RESOURCE, + VENDORED_RESOURCE_KIND, + feed_identity, + load_vendor_selections, + load_vendored_resource, + rows_matching, + validate_vendor_selections, + vendor_resource, + vendor_row, +) +from microcosm.build.uk_runtime.national_chronicle_feed import ( + load_uk_national_chronicle_feed, +) + +ROOT = Path(__file__).resolve().parents[3] +STABLE_UK_FACT_FEED_NAME = ".codex-work/consumer_facts_uk.jsonl" + + +def _package_dir() -> Path: + return Path( + str( + importlib_resources.files("microcosm.build.uk").joinpath( + "country_package.json" + ) + ) + ).parent + + +def _country_package_paths() -> set[str]: + package = json.loads((_package_dir() / "country_package.json").read_text()) + return {row["path"] for row in package["resources"]} + + +def _pinned_feed(tmp_path: Path): + configured = os.environ.get("CHRONICLE_UK_FACTS") + feed = Path(configured) if configured else ROOT / STABLE_UK_FACT_FEED_NAME + if not feed.exists(): + pytest.skip("pinned UK Chronicle national consumer feed is not present") + if feed.is_dir(): + artifact_path = feed + else: + manifest = ( + ROOT / ".codex-work/consumer_facts_uk_manifest.json" + if not configured + else feed.with_name("manifest.json") + ) + if not manifest.is_file(): + pytest.skip("pinned UK Chronicle consumer manifest is not present") + artifact_path = tmp_path / "consumer-artifact" + artifact_path.mkdir() + (artifact_path / "consumer_facts.jsonl").symlink_to(feed.resolve()) + (artifact_path / "manifest.json").symlink_to(manifest.resolve()) + pin = load_uk_national_chronicle_feed() + return artifact_path, load_ledger_consumer_artifact( + artifact_path, + expected_facts_sha256=pin.facts_sha256, + expected_manifest_sha256=pin.manifest_sha256, + ) + + +def test_selection_register_is_declared_and_well_formed() -> None: + register = load_vendor_selections() + assert VENDOR_SELECTIONS_RESOURCE in _country_package_paths() + names = [entry["resource"] for entry in register["resources"]] + assert len(names) == len(set(names)) + for entry in register["resources"]: + assert entry["resource"] in _country_package_paths(), entry["resource"] + + +@pytest.mark.parametrize( + "mutation", + [ + {"schema_version": 2}, + {"kind": "something_else"}, + {"resources": []}, + ], +) +def test_selection_register_refuses_bad_shapes(mutation) -> None: + register = json.loads(json.dumps(load_vendor_selections())) + register.update(mutation) + with pytest.raises(ValueError): + validate_vendor_selections(register) + + +def test_selection_register_refuses_unknown_selector_fields() -> None: + register = json.loads(json.dumps(load_vendor_selections())) + register["resources"][0]["selections"][0]["selector"]["not_a_field"] = "x" + with pytest.raises(ValueError, match="unsupported selector field"): + validate_vendor_selections(register) + + +def test_committed_vendored_resources_carry_the_national_pin_and_declared_counts() -> ( + None +): + register = load_vendor_selections() + pin = load_uk_national_chronicle_feed() + for entry in register["resources"]: + payload = load_vendored_resource(entry["resource"]) + assert payload["kind"] == VENDORED_RESOURCE_KIND + assert payload["source_fact_feed"] == feed_identity(pin) + by_label = {row["label"]: row for row in payload["selections"]} + assert set(by_label) == {row["label"] for row in entry["selections"]} + for selection in entry["selections"]: + assert ( + by_label[selection["label"]]["row_count"] + == selection["expected_row_count"] + ) + assert by_label[selection["label"]]["selector"] == selection["selector"] + keys = [row["aggregate_fact_key"] for row in payload["rows"]] + assert len(keys) == len(set(keys)) + assert payload["row_count"] == len(keys) + assert all(isinstance(row["value"], (int, float)) for row in payload["rows"]) + + +def test_vendor_row_copies_keys_provenance_and_value_verbatim() -> None: + fact = { + "aggregate_fact_key": "ledger.aggregate_fact.v2:abc", + "semantic_fact_key": "ledger.semantic_fact.v2:def", + "aggregation": {"method": "sum"}, + "dimensions": {"b": 2, "a": "x"}, + "entity": {"name": "household", "role": "r"}, + "geography": {"id": "E92000001", "level": "country", "vintage": "current"}, + "layout": { + "record_set_id": "rs", + "groupby_dimension": "g", + "groupby_value_id": "v", + "table_record_kind": "total", + }, + "lineage": {"source_record_id": "src.rec"}, + "observed_measure": { + "source_concept": "x.y", + "source_measure_id": "m", + "unit": "gbp", + }, + "period": {"type": "fiscal_year", "value": 2024}, + "period_coverage": { + "start_date": "2024-04-01", + "end_date": "2025-03-31", + "basis": "fiscal", + }, + "source": { + "source_name": "x", + "source_table": "t", + "source_file": "f.ods", + "source_sha256": "0" * 64, + "vintage": "v1", + "raw_r2_key": "raw/x/x-pkg/2025/0/f.ods", + }, + "value": 12.5, + } + row = vendor_row(fact) + assert row["aggregate_fact_key"] == "ledger_aggregate_fact_v2_abc" + assert row["semantic_fact_key"] == "ledger_semantic_fact_v2_def" + assert row["value"] == 12.5 + assert row["concept"] == "x.y" + assert list(row["dimensions"]) == ["a", "b"] + assert row["source"]["source_sha256"] == "0" * 64 + assert row["period"] == {"type": "fiscal_year", "value": 2024} + with pytest.raises(ValueError, match="non-numeric"): + vendor_row({**fact, "value": "12"}) + + +def test_vendor_resource_refuses_count_drift() -> None: + fact = { + "aggregate_fact_key": "ledger.aggregate_fact.v2:one", + "semantic_fact_key": "ledger.semantic_fact.v2:one", + "observed_measure": {"source_concept": "x.y", "source_name": "x"}, + "geography": {"id": "K02000001", "level": "country"}, + "period": {"type": "calendar_year", "value": 2024}, + "layout": {}, + "value": 1.0, + } + entry = { + "resource": "probe.json", + "purpose": "p", + "consumers": ["c"], + "selections": [ + { + "label": "s", + "selector": {"source_concept": "x.y"}, + "expected_row_count": 2, + } + ], + } + pin = load_uk_national_chronicle_feed() + with pytest.raises(ValueError, match="matched 1 facts, expected 2"): + vendor_resource(entry, [fact], pin=pin) + relaxed = vendor_resource(entry, [fact], pin=pin, strict_counts=False) + assert relaxed.row_count == 1 + assert relaxed.payload["selections"][0]["row_count"] == 1 + + +def test_rows_matching_filters_by_alias_and_membership() -> None: + payload = load_vendored_resource("dft_bus_value_anchors.json") + london = rows_matching( + payload, + concept="dft.local_bus_passenger_fare_receipts", + geography_id="E12000007", + period_value=2025, + ) + assert len(london) == 1 + assert london[0]["value"] == pytest.approx(1_347_434_943.01459) + areas = rows_matching( + payload, + concept="dft.local_bus_total_estimated_net_support", + period_value=[2025], + ) + assert {row["geography"]["id"] for row in areas} == { + "E92000001", + "E12000007", + "dft:england_outside_london", + } + + +def test_committed_vendored_resources_regenerate_from_pinned_feed( + tmp_path: Path, +) -> None: + artifact_path, artifact = _pinned_feed(tmp_path) + generated = tmp_path / "generated" + generated.mkdir() + completed = subprocess.run( + [ + sys.executable, + str(ROOT / "tools" / "vendor_uk_ledger_facts.py"), + "--ledger-facts", + str(artifact_path), + "--ledger-facts-sha256", + artifact.facts_sha256, + "--output-dir", + str(generated), + ], + check=True, + capture_output=True, + text=True, + ) + assert "wrote" in completed.stdout + for entry in load_vendor_selections()["resources"]: + committed = (_package_dir() / entry["resource"]).read_bytes() + regenerated = (generated / entry["resource"]).read_bytes() + assert ( + hashlib.sha256(committed).hexdigest() + == hashlib.sha256(regenerated).hexdigest() + ), entry["resource"] diff --git a/tools/vendor_uk_ledger_facts.py b/tools/vendor_uk_ledger_facts.py new file mode 100644 index 000000000..e1aef6a1a --- /dev/null +++ b/tools/vendor_uk_ledger_facts.py @@ -0,0 +1,123 @@ +#!/usr/bin/env python +"""Vendor pinned Chronicle facts into UK build resources. + +Offline authoring tool: it reads an already-exported Chronicle consumer +artifact, checks it against the committed national feed pin, and copies the +facts named by ``uk/ledger_fact_vendor_selections.json`` into per-concern JSON +resources under the UK country package. It never derives a value. + + uv run --no-sync python tools/vendor_uk_ledger_facts.py \\ + --ledger-facts .codex-work/uk-artifact [--check] [--resource NAME ...] +""" + +from __future__ import annotations + +import argparse +import sys +from importlib.resources import files +from pathlib import Path + +from microcosm.build.ledger_artifact import ( + add_ledger_artifact_args, + resolve_ledger_artifact, +) +from microcosm.build.uk_runtime.ledger_fact_vendoring import ( + load_vendor_selections, + vendor_all, +) +from microcosm.build.uk_runtime.national_chronicle_feed import ( + load_uk_national_chronicle_feed, +) + + +def _parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser(description=__doc__) + add_ledger_artifact_args(parser) + parser.add_argument( + "--output-dir", + type=Path, + default=None, + help="Directory to write resources into (default: the packaged uk/ directory).", + ) + parser.add_argument( + "--resource", + action="append", + default=None, + help="Vendor only this resource (repeatable); default is every register entry.", + ) + parser.add_argument( + "--check", + action="store_true", + help="Do not write; exit 1 if any committed resource differs from the regeneration.", + ) + parser.add_argument( + "--allow-unpinned-feed", + action="store_true", + help="Vendor from a feed whose digests differ from the national pin (diagnostic only).", + ) + parser.add_argument( + "--report-counts", + action="store_true", + help="Print matched row counts per selection without enforcing expected_row_count.", + ) + return parser + + +def main() -> int: + args = _parser().parse_args() + artifact = resolve_ledger_artifact(args) + if artifact is None: + raise SystemExit("error: --ledger-facts is required.") + pin = load_uk_national_chronicle_feed() + if artifact.facts_sha256 != pin.facts_sha256 or ( + artifact.manifest_sha256 is not None + and artifact.manifest_sha256 != pin.manifest_sha256 + ): + message = ( + "Chronicle consumer facts differ from the committed UK national feed pin: " + f"loaded facts {artifact.facts_sha256}, committed {pin.facts_sha256}." + ) + if not args.allow_unpinned_feed: + raise SystemExit( + f"error: {message} Re-pin first, or pass --allow-unpinned-feed for a diagnostic run." + ) + print(f"warning: {message} (--allow-unpinned-feed)", file=sys.stderr) + register = load_vendor_selections() + output_dir = ( + args.output_dir + or Path( + str(files("microcosm.build.uk").joinpath("country_package.json")) + ).parent + ) + results = vendor_all( + register, + artifact.facts, + pin=pin, + only=args.resource, + strict_counts=not args.report_counts, + ) + drift = 0 + for result in results: + target = output_dir / result.resource + if args.report_counts: + for selection in result.payload["selections"]: + print( + f"{result.resource} {selection['label']}: {selection['row_count']} rows" + ) + if args.check: + current = target.read_bytes() if target.exists() else b"" + status = "ok" if current == result.content else "DRIFT" + if status == "DRIFT": + drift += 1 + print( + f"{status} {result.resource} rows={result.row_count} sha256={result.sha256}" + ) + continue + if not args.report_counts: + target.write_bytes(result.content) + print(f"wrote {target} rows={result.row_count} sha256={result.sha256}") + return 1 if drift else 0 + + +if __name__ == "__main__": + raise SystemExit(main()) From 5e404bab9313a438f3aed8375271c0bba429029f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mar=C3=ADa=20Juaristi?= <127882282+juaristi22@users.noreply.github.com> Date: Fri, 11 Sep 2026 12:55:02 +0200 Subject: [PATCH 03/10] Bind the DfT London bus rows at region level and derive England outside London (#890 B) The national reference generator now pins a contract target whose only declared level is region at region level with its E12 code, so the two London bus rows match the region-stamped BUS05i facts (fares GBP 1,347.4m, net support GBP 1,130.2m, year ending March 2025) instead of a country row that never exists. The compiler refuses a region pin outside the nine English region codes the geography ladder already carries and refuses a region-pinned reference that resolves anything but a region-stamped fact, so no second roster is needed. England outside London is now a derived partition member: the England and London rows are bound, Chronicle stamps the outside-London rows at statistical_scope, and test_uk_bus_targets keeps asserting the publisher partition reconciles within GBP 1,000. The UK rows stay signed out because Wales publishes no fare receipts. Active references 415 -> 417; the compile-parity receipts gain the two London rows as ledger-only entries. Contract notes on the England rows now describe the compile-time BUS0415 alignment and the in-stage NTS0705a shaping rather than "pending #790". Co-Authored-By: Claude Fable 5.1 --- ...ity_incumbent_2025_signed_differences.json | 20 ++- ...ty_production_2023_signed_differences.json | 20 ++- .../build/uk/target_reference_membership.json | 50 +++++--- .../target_reference_signed_exclusions.json | 118 ++++++++---------- .../microcosm/build/uk/target_references.json | 46 +++++++ .../build/uk/uk_population_targets.json | 4 +- .../build/uk_runtime/ledger_targets.py | 46 +++++++ .../tests/test_uk_bus_targets.py | 92 +++++++++++++- .../tests/test_uk_target_references.py | 32 ++++- tools/generate_uk_target_references.py | 28 ++++- 10 files changed, 349 insertions(+), 107 deletions(-) diff --git a/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_incumbent_2025_signed_differences.json b/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_incumbent_2025_signed_differences.json index 560699408..9acb3f522 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_incumbent_2025_signed_differences.json +++ b/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_incumbent_2025_signed_differences.json @@ -1,11 +1,11 @@ { - "compiled_count": 415, + "compiled_count": 417, "counts_by_kind": { "calibration_drift": 172, "fixture_only": 268, - "ledger_only": 46 + "ledger_only": 48 }, - "difference_count": 486, + "difference_count": 488, "differences": [ { "fixture_value": 1608000.0, @@ -1911,6 +1911,13 @@ "period": 2025, "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." }, + { + "kind": "ledger_only", + "ledger_value": 1347434943.01459, + "name": "dft.bus_fare_receipts.london", + "period": 2025, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, { "kind": "ledger_only", "ledger_value": 3024904320.8399997, @@ -1918,6 +1925,13 @@ "period": 2025, "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." }, + { + "kind": "ledger_only", + "ledger_value": 1130214000.0, + "name": "dft.bus_net_support.london", + "period": 2025, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, { "kind": "ledger_only", "ledger_value": 597920.0, diff --git a/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_production_2023_signed_differences.json b/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_production_2023_signed_differences.json index cd4d9c7fb..6860a29fe 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_production_2023_signed_differences.json +++ b/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_production_2023_signed_differences.json @@ -1,11 +1,11 @@ { - "compiled_count": 230, + "compiled_count": 232, "counts_by_kind": { "calibration_drift": 12, "fixture_only": 125, - "ledger_only": 206 + "ledger_only": 208 }, - "difference_count": 343, + "difference_count": 345, "differences": [ { "fixture_value": 82000.0, @@ -903,6 +903,13 @@ "period": 2023, "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." }, + { + "kind": "ledger_only", + "ledger_value": 1342087642.33642, + "name": "dft.bus_fare_receipts.london", + "period": 2023, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, { "kind": "ledger_only", "ledger_value": 2651721003.6800003, @@ -910,6 +917,13 @@ "period": 2023, "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." }, + { + "kind": "ledger_only", + "ledger_value": 954406918.7, + "name": "dft.bus_net_support.london", + "period": 2023, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, { "kind": "ledger_only", "ledger_value": 6197311.0, diff --git a/packages/microcosm-build/src/microcosm/build/uk/target_reference_membership.json b/packages/microcosm-build/src/microcosm/build/uk/target_reference_membership.json index e49ba5496..47adb1575 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/target_reference_membership.json +++ b/packages/microcosm-build/src/microcosm/build/uk/target_reference_membership.json @@ -3,11 +3,11 @@ "target_period": 2025, "candidate_count": 430, "contract_target_count": 202, - "active_reference_count": 415, + "active_reference_count": 417, "status_counts": { - "active": 415, + "active": 417, "no_fact_at_or_before_period": 7, - "signed_excluded": 8 + "signed_excluded": 6 }, "geography_pins": { "obr.income_tax": { @@ -795,11 +795,11 @@ "geography_id": "E92000001" }, "dft.bus_fare_receipts.london": { - "geography_level": "country", + "geography_level": "region", "geography_id": "E12000007" }, "dft.bus_net_support.london": { - "geography_level": "country", + "geography_level": "region", "geography_id": "E12000007" }, "dft.bus_fare_receipts.england_outside_london": { @@ -2683,6 +2683,16 @@ "name": "dft.bus_net_support.england", "from": "2024", "to": "2025" + }, + { + "name": "dft.bus_fare_receipts.london", + "from": "2024", + "to": "2025" + }, + { + "name": "dft.bus_net_support.london", + "from": "2024", + "to": "2025" } ], "targets": { @@ -7890,26 +7900,32 @@ ] }, "dft.bus_fare_receipts.london": { - "status": "signed_excluded", + "status": "active", "candidates": [ { "name": "dft.bus_fare_receipts.london", - "status": "signed_excluded", - "matched_fact_count_overall": 0, - "matched_fact_count_at_or_before_period": 0, - "signed_rationale": "Region target held until the UK target compiler has a declared region roster and cross-grain leg; tracked by #790." + "status": "active", + "matched_fact_count_overall": 3, + "matched_fact_count_at_or_before_period": 3, + "resolved_period": 2025, + "resolved_value": 1347434943.01459, + "resolved_fact_period": "2024", + "resolved_fact_key": "ledger_aggregate_fact_v2_33611c64d2048fe660501802" } ] }, "dft.bus_net_support.london": { - "status": "signed_excluded", + "status": "active", "candidates": [ { "name": "dft.bus_net_support.london", - "status": "signed_excluded", - "matched_fact_count_overall": 0, - "matched_fact_count_at_or_before_period": 0, - "signed_rationale": "Region target held until the UK target compiler has a declared region roster and cross-grain leg; tracked by #790." + "status": "active", + "matched_fact_count_overall": 3, + "matched_fact_count_at_or_before_period": 3, + "resolved_period": 2025, + "resolved_value": 1130214000.0, + "resolved_fact_period": "2024", + "resolved_fact_key": "ledger_aggregate_fact_v2_d8e40cee2fc723b719ff4680" } ] }, @@ -7921,7 +7937,7 @@ "status": "signed_excluded", "matched_fact_count_overall": 0, "matched_fact_count_at_or_before_period": 0, - "signed_rationale": "Chronicle stamps this row at statistical_scope; Microcosm has no declared statistical-scope pin and must not bind the England total accidentally." + "signed_rationale": "England outside London is England minus London: the England row and the London row (pinned at region E12000007) are both bound, so this publisher partition member is derived rather than pinned; Chronicle stamps it at statistical_scope and test_uk_bus_targets asserts the partition reconciles to England within GBP 1,000." } ] }, @@ -7933,7 +7949,7 @@ "status": "signed_excluded", "matched_fact_count_overall": 0, "matched_fact_count_at_or_before_period": 0, - "signed_rationale": "Chronicle stamps this row at statistical_scope; Microcosm has no declared statistical-scope pin and must not bind the England total accidentally." + "signed_rationale": "England outside London is England minus London: the England row and the London row (pinned at region E12000007) are both bound, so this publisher partition member is derived rather than pinned; Chronicle stamps it at statistical_scope and test_uk_bus_targets asserts the partition reconciles to England within GBP 1,000." } ] }, diff --git a/packages/microcosm-build/src/microcosm/build/uk/target_reference_signed_exclusions.json b/packages/microcosm-build/src/microcosm/build/uk/target_reference_signed_exclusions.json index 5981d7d25..ae052d712 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/target_reference_signed_exclusions.json +++ b/packages/microcosm-build/src/microcosm/build/uk/target_reference_signed_exclusions.json @@ -1,69 +1,53 @@ { - "schema_version": 1, - "exclusions": [ - { - "target_id": "hmrc.spi.property_income.amount_by_total_income_band", - "reason_id": "property_income_undercount_adjustment_missing", - "rationale": "Signed out pending a first-class value-scaling operation or a Chronicle package for HMRC Property Rental Income Statistics with declared reconciliation: the Ledger facts are official HMRC SPI Table 3.7 net property-income amounts, while the incumbent calibration target applies the populace-side x1.9 property-income undercount adjustment. The x1.9 trace is uk-data PR #311 / issue uk-data#230: SPI covers only taxpayers with liability, and HMRC Property Rental Income Statistics show GBP 46.68bn versus SPI about GBP 24.5bn for 2020-21. Binding the raw SPI facts would knowingly calibrate to 10/19 of the incumbent surface.", - "tracking": "uk-data#230", - "approved_by": "juaristi22", - "approved_on": "2026-09-05" - }, - { - "target_id": "ons.population.scotland_households_3plus_children", - "reason_id": "no_household_composition_fact", - "rationale": "Signed out pending a Scotland household-composition fact: the current selector reaches person-level ONS mid-year population age rows, not households with three or more children. microcosm#736 tracks the missing declaration.", - "tracking": "microcosm#736", - "approved_by": "juaristi22", - "approved_on": "2026-09-05" - }, - { - "target_id": "dft.bus_fare_receipts.london", - "reason_id": "region_pin_unsupported", - "rationale": "Region target held until the UK target compiler has a declared region roster and cross-grain leg; tracked by #790.", - "tracking": "microcosm#790", - "approved_by": "juaristi22", - "approved_on": "2026-09-05" - }, - { - "target_id": "dft.bus_net_support.london", - "reason_id": "region_pin_unsupported", - "rationale": "Region target held until the UK target compiler has a declared region roster and cross-grain leg; tracked by #790.", - "tracking": "microcosm#790", - "approved_by": "juaristi22", - "approved_on": "2026-09-05" - }, - { - "target_id": "dft.bus_fare_receipts.england_outside_london", - "reason_id": "statistical_scope_unsupported", - "rationale": "Chronicle stamps this row at statistical_scope; Microcosm has no declared statistical-scope pin and must not bind the England total accidentally.", - "tracking": "microcosm#789", - "approved_by": "juaristi22", - "approved_on": "2026-09-05" - }, - { - "target_id": "dft.bus_net_support.england_outside_london", - "reason_id": "statistical_scope_unsupported", - "rationale": "Chronicle stamps this row at statistical_scope; Microcosm has no declared statistical-scope pin and must not bind the England total accidentally.", - "tracking": "microcosm#789", - "approved_by": "juaristi22", - "approved_on": "2026-09-05" - }, - { - "target_id": "dft.bus_fare_receipts.uk", - "reason_id": "no_publisher_uk_total", - "rationale": "BUS05i publishes no UK total and Microcosm declares no method to construct one.", - "tracking": "microcosm#789", - "approved_by": "juaristi22", - "approved_on": "2026-09-05" - }, - { - "target_id": "dft.bus_net_support.uk", - "reason_id": "no_publisher_uk_total", - "rationale": "BUS05i publishes no UK total and Microcosm declares no method to construct one.", - "tracking": "microcosm#789", - "approved_by": "juaristi22", - "approved_on": "2026-09-05" - } - ] + "schema_version": 1, + "exclusions": [ + { + "target_id": "hmrc.spi.property_income.amount_by_total_income_band", + "reason_id": "property_income_undercount_adjustment_missing", + "rationale": "Signed out pending a first-class value-scaling operation or a Chronicle package for HMRC Property Rental Income Statistics with declared reconciliation: the Ledger facts are official HMRC SPI Table 3.7 net property-income amounts, while the incumbent calibration target applies the populace-side x1.9 property-income undercount adjustment. The x1.9 trace is uk-data PR #311 / issue uk-data#230: SPI covers only taxpayers with liability, and HMRC Property Rental Income Statistics show GBP 46.68bn versus SPI about GBP 24.5bn for 2020-21. Binding the raw SPI facts would knowingly calibrate to 10/19 of the incumbent surface.", + "tracking": "uk-data#230", + "approved_by": "juaristi22", + "approved_on": "2026-09-05" + }, + { + "target_id": "ons.population.scotland_households_3plus_children", + "reason_id": "no_household_composition_fact", + "rationale": "Signed out pending a Scotland household-composition fact: the current selector reaches person-level ONS mid-year population age rows, not households with three or more children. microcosm#736 tracks the missing declaration.", + "tracking": "microcosm#736", + "approved_by": "juaristi22", + "approved_on": "2026-09-05" + }, + { + "target_id": "dft.bus_fare_receipts.england_outside_london", + "reason_id": "derived_partition_member", + "rationale": "England outside London is England minus London: the England row and the London row (pinned at region E12000007) are both bound, so this publisher partition member is derived rather than pinned; Chronicle stamps it at statistical_scope and test_uk_bus_targets asserts the partition reconciles to England within GBP 1,000.", + "tracking": "microcosm#890", + "approved_by": "juaristi22", + "approved_on": "2026-09-11" + }, + { + "target_id": "dft.bus_net_support.england_outside_london", + "reason_id": "derived_partition_member", + "rationale": "England outside London is England minus London: the England row and the London row (pinned at region E12000007) are both bound, so this publisher partition member is derived rather than pinned; Chronicle stamps it at statistical_scope and test_uk_bus_targets asserts the partition reconciles to England within GBP 1,000.", + "tracking": "microcosm#890", + "approved_by": "juaristi22", + "approved_on": "2026-09-11" + }, + { + "target_id": "dft.bus_fare_receipts.uk", + "reason_id": "no_publisher_uk_total", + "rationale": "BUS05i publishes no UK total and Microcosm declares no method to construct one.", + "tracking": "microcosm#789", + "approved_by": "juaristi22", + "approved_on": "2026-09-05" + }, + { + "target_id": "dft.bus_net_support.uk", + "reason_id": "no_publisher_uk_total", + "rationale": "BUS05i publishes no UK total and Microcosm declares no method to construct one.", + "tracking": "microcosm#789", + "approved_by": "juaristi22", + "approved_on": "2026-09-05" + } + ] } diff --git a/packages/microcosm-build/src/microcosm/build/uk/target_references.json b/packages/microcosm-build/src/microcosm/build/uk/target_references.json index 8ab2aeccd..93f30fa9c 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/target_references.json +++ b/packages/microcosm-build/src/microcosm/build/uk/target_references.json @@ -11159,6 +11159,52 @@ }, "uprating_from_period": "2024", "uprating_to_period": 2025 + }, + { + "name": "dft.bus_fare_receipts.london", + "ledger_selector": { + "source_name": "dft", + "source_concept": "dft.local_bus_passenger_fare_receipts", + "layout_groupby_value_id": "london", + "entity_name": "institutional_sector", + "period_type": "fiscal_year", + "geography_level": "region", + "geography_id": "E12000007" + }, + "entity": "household", + "measure": "dft/bus_fare_receipts/london", + "family": "dft_local_bus", + "period": 2025, + "metadata": { + "contract_target_id": "dft.bus_fare_receipts.london", + "measure_kind": "prepared_column", + "observation_basis": "fiscal_year_flow" + }, + "uprating_from_period": "2024", + "uprating_to_period": 2025 + }, + { + "name": "dft.bus_net_support.london", + "ledger_selector": { + "source_name": "dft", + "source_concept": "dft.local_bus_total_estimated_net_support", + "layout_groupby_value_id": "london", + "entity_name": "institutional_sector", + "period_type": "fiscal_year", + "geography_level": "region", + "geography_id": "E12000007" + }, + "entity": "household", + "measure": "dft/bus_net_support/london", + "family": "dft_local_bus", + "period": 2025, + "metadata": { + "contract_target_id": "dft.bus_net_support.london", + "measure_kind": "prepared_column", + "observation_basis": "fiscal_year_flow" + }, + "uprating_from_period": "2024", + "uprating_to_period": 2025 } ] } diff --git a/packages/microcosm-build/src/microcosm/build/uk/uk_population_targets.json b/packages/microcosm-build/src/microcosm/build/uk/uk_population_targets.json index c81588dd6..9b2517a39 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/uk_population_targets.json +++ b/packages/microcosm-build/src/microcosm/build/uk/uk_population_targets.json @@ -12312,7 +12312,7 @@ "equals": "ENGLAND" } ], - "notes": "DfT closing-year label: period 2025 means YE March 2025. BUS05i is consumed; NTS0705a and BUS0415 are not consumed pending #790." + "notes": "DfT closing-year label: period 2025 means YE March 2025. BUS05i is consumed. The London row binds at region level (E12000007) and England outside London is derived, so the partition is checked, not double-bound. BUS0415 aligns the fare receipts to the calibration year in the compile pass; NTS0705a shapes the imputed column inside the LCFS stage (#890)." }, "axiom": { "metric_name": "dft/bus_fare_receipts/england", @@ -12355,7 +12355,7 @@ "equals": "ENGLAND" } ], - "notes": "DfT closing-year label: period 2025 means YE March 2025. BUS05i is consumed; BUS0415 is not selectable for support." + "notes": "DfT closing-year label: period 2025 means YE March 2025. BUS05i is consumed; the London row binds at region level (E12000007) and England outside London is derived. BUS0415 is never applied to net support (#789 scope item 5)." }, "axiom": { "metric_name": "dft/bus_net_support/england", diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/ledger_targets.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/ledger_targets.py index 2541618d9..7f44fad72 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/ledger_targets.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/ledger_targets.py @@ -29,6 +29,7 @@ materialize_target_bindings, ) from microcosm.build.uk_runtime.cgt_calibration import uk_cgt_annual_exempt_amount +from microcosm.build.uk_runtime.geography_ladder import UK_ENGLAND_WALES_REGION_CODES from microcosm.build.uk_runtime.local_target_census import family_for_metric from microcosm.build.uk_runtime.local_targets import ( AREA_TYPE_TO_LEDGER_GEOGRAPHY_LEVEL, @@ -254,11 +255,13 @@ def compile_uk_target_registry( ) candidate_facts = _candidate_facts_for_reference(fact_rows, restamped) try: + _assert_national_region_pin(restamped) registry = compile_ledger_target_references( candidate_facts, [restamped], country="uk", ) + registry = _assert_region_facts_resolved_at_region(restamped, registry) registry = validate_uc_source_month_coverage( restamped, registry, candidate_facts ) @@ -287,6 +290,49 @@ def compile_uk_target_registry( ) +#: National references may pin a region only from the published English +#: region roster; the codes are the ladder's own (geography_ladder), so no +#: second register carries them. +UK_NATIONAL_REGION_ROSTER: frozenset[str] = frozenset( + code for code in UK_ENGLAND_WALES_REGION_CODES if code.startswith("E12") +) + + +def _assert_national_region_pin(reference: LedgerTargetReference) -> None: + """Refuse a region-pinned national reference outside the region roster.""" + + selector = reference.ledger_selector + level = str(selector.get("geography_level") or "") + if level != "region": + return + geography_id = str(selector.get("geography_id") or "") + if geography_id not in UK_NATIONAL_REGION_ROSTER: + raise ValueError( + f"UK national reference {reference.name!r} pins region " + f"{geography_id!r}, which is not in the English region roster " + f"{sorted(UK_NATIONAL_REGION_ROSTER)}." + ) + + +def _assert_region_facts_resolved_at_region( + reference: LedgerTargetReference, registry: TargetRegistry +) -> TargetRegistry: + """A region-pinned reference must resolve a region-stamped fact.""" + + if str(reference.ledger_selector.get("geography_level") or "") != "region": + return registry + for spec in registry.specs: + level = str(spec.metadata.get("ledger_geography_level") or "") + geography_id = str(spec.metadata.get("ledger_geography_id") or "") + if level != "region" or geography_id not in UK_NATIONAL_REGION_ROSTER: + raise ValueError( + f"UK national reference {reference.name!r} resolved a fact at " + f"{level!r} {geography_id!r}; a region pin must resolve a " + "region-stamped fact inside the roster." + ) + return registry + + def _cgt_cash_diagnostic_metadata( facts: tuple[Mapping[str, Any], ...], ) -> dict[str, str]: diff --git a/packages/microcosm-build/tests/test_uk_bus_targets.py b/packages/microcosm-build/tests/test_uk_bus_targets.py index 57f0988b4..33d4969f7 100644 --- a/packages/microcosm-build/tests/test_uk_bus_targets.py +++ b/packages/microcosm-build/tests/test_uk_bus_targets.py @@ -42,7 +42,8 @@ def test_bus05i_fy2025_subareas_reconcile_to_england() -> None: def test_bus_targets_are_active_or_signed_excluded_as_declared() -> None: contract = _resource("uk_population_targets.json") references = { - row["name"] for row in _resource("target_references.json")["target_references"] + row["name"]: row + for row in _resource("target_references.json")["target_references"] } membership = _resource("target_reference_membership.json") exclusions = { @@ -55,21 +56,51 @@ def test_bus_targets_are_active_or_signed_excluded_as_declared() -> None: if row["target_id"].startswith(BUS_PREFIX) } - active = {"dft.bus_fare_receipts.england", "dft.bus_net_support.england"} + active = { + "dft.bus_fare_receipts.england", + "dft.bus_net_support.england", + "dft.bus_fare_receipts.london", + "dft.bus_net_support.london", + } excluded = set(bus) - active assert len(bus) == 8 - assert active <= references + assert active <= set(references) assert excluded.isdisjoint(references) assert set(exclusions) >= excluded assert {exclusions[target_id]["reason_id"] for target_id in excluded} == { - "region_pin_unsupported", - "statistical_scope_unsupported", + "derived_partition_member", "no_publisher_uk_total", } assert all( membership["targets"][target_id]["status"] == "signed_excluded" for target_id in excluded ) + # The London rows pin the publisher's region-stamped fact, not a country + # row, and the England rows keep the country pin. + for target_id in ("dft.bus_fare_receipts.london", "dft.bus_net_support.london"): + selector = references[target_id]["ledger_selector"] + assert selector["geography_level"] == "region" + assert selector["geography_id"] == "E12000007" + assert membership["targets"][target_id]["status"] == "active" + for target_id in ("dft.bus_fare_receipts.england", "dft.bus_net_support.england"): + selector = references[target_id]["ledger_selector"] + assert selector["geography_level"] == "country" + assert selector["geography_id"] == "E92000001" + + +def test_london_bus_rows_resolve_the_publisher_partition() -> None: + fixture = json.loads(FIXTURE.read_text()) + published = { + (row["source_concept"], row["groupby_value_id"]): row["value"] + for row in fixture["rows"] + } + membership = _resource("target_reference_membership.json")["targets"] + for target_id, concept in ( + ("dft.bus_fare_receipts.london", "dft.local_bus_passenger_fare_receipts"), + ("dft.bus_net_support.london", "dft.local_bus_total_estimated_net_support"), + ): + resolved = membership[target_id]["candidates"][0]["resolved_value"] + assert resolved == pytest.approx(published[(concept, "london")], abs=1.0) def test_bus_selectors_do_not_consume_nts_or_bus0415() -> None: @@ -93,4 +124,53 @@ def test_bus_selectors_do_not_consume_nts_or_bus0415() -> None: ) assert "NTS0705a" in england_notes assert "BUS0415" in england_notes - assert "#790" in england_notes + assert "#890" in england_notes + assert "#790" not in england_notes + + +def test_generator_pins_region_only_targets_at_region_level() -> None: + from tools.generate_uk_target_references import _geography_pins + + pins = _geography_pins(_resource("uk_population_targets.json")) + assert pins["dft.bus_fare_receipts.london"] == { + "geography_level": "region", + "geography_id": "E12000007", + } + assert pins["dft.bus_fare_receipts.england"] == { + "geography_level": "country", + "geography_id": "E92000001", + } + assert pins["dft.bus_fare_receipts.england_outside_london"]["geography_level"] == ( + "country" + ) + + +def test_compiler_refuses_region_pins_outside_the_english_region_roster() -> None: + from microcosm.build.ledger_targets import LedgerTargetReference + from microcosm.build.uk_runtime.ledger_targets import ( + UK_NATIONAL_REGION_ROSTER, + _assert_national_region_pin, + ) + + assert UK_NATIONAL_REGION_ROSTER == frozenset( + f"E1200000{index}" for index in range(1, 10) + ) + good = LedgerTargetReference( + name="probe", + ledger_selector={"geography_level": "region", "geography_id": "E12000007"}, + entity="household", + measure="probe", + family="dft_local_bus", + period=2025, + ) + _assert_national_region_pin(good) + bad = LedgerTargetReference( + name="probe", + ledger_selector={"geography_level": "region", "geography_id": "W99999999"}, + entity="household", + measure="probe", + family="dft_local_bus", + period=2025, + ) + with pytest.raises(ValueError, match="not in the English region roster"): + _assert_national_region_pin(bad) diff --git a/packages/microcosm-build/tests/test_uk_target_references.py b/packages/microcosm-build/tests/test_uk_target_references.py index 6ef3a3c6c..940a30af0 100644 --- a/packages/microcosm-build/tests/test_uk_target_references.py +++ b/packages/microcosm-build/tests/test_uk_target_references.py @@ -56,7 +56,7 @@ _value_operation_by_target_id, ) -ACTIVE_REFERENCE_COUNT = 415 +ACTIVE_REFERENCE_COUNT = 417 UK_DATA_REPO = "policyengine-" + "uk-data" @@ -239,7 +239,14 @@ def test_uk_target_references_follow_contract_derivation_rules() -> None: assert value.items() <= reference["ledger_selector"][key].items() continue assert reference["ledger_selector"][key] == value - assert reference["ledger_selector"]["geography_level"] == "country" + # Region-only contract targets (the DfT London bus rows) pin the + # publisher's region-stamped fact; everything else pins a country. + expected_level = ( + "region" + if list(target.get("geography_levels") or ()) == ["region"] + else "country" + ) + assert reference["ledger_selector"]["geography_level"] == expected_level assert reference["ledger_selector"]["geography_id"] assert reference["entity"] == _expected_reference_entity(target) expected_measure = ( @@ -306,6 +313,8 @@ def test_childcare_and_bus_references_compile_with_declared_provenance() -> None "dfe.funded_childcare.universal_only_children": 396_965.0, "dft.bus_fare_receipts.england": 3_417_388_656.43538, "dft.bus_net_support.england": 3_024_904_320.8399997, + "dft.bus_fare_receipts.london": 1_347_434_943.01459, + "dft.bus_net_support.london": 1_130_214_000.0, } assert { name: spec.metadata["ledger_entity_name"] for name, spec in specs.items() @@ -317,6 +326,21 @@ def test_childcare_and_bus_references_compile_with_declared_provenance() -> None "dfe.funded_childcare.universal_only_children": "person", "dft.bus_fare_receipts.england": "institutional_sector", "dft.bus_net_support.england": "institutional_sector", + "dft.bus_fare_receipts.london": "institutional_sector", + "dft.bus_net_support.london": "institutional_sector", + } + assert { + name: ( + spec.metadata["ledger_geography_level"], + spec.metadata["ledger_geography_id"], + ) + for name, spec in specs.items() + if name.startswith("dft.") + } == { + "dft.bus_fare_receipts.england": ("country", "E92000001"), + "dft.bus_net_support.england": ("country", "E92000001"), + "dft.bus_fare_receipts.london": ("region", "E12000007"), + "dft.bus_net_support.london": ("region", "E12000007"), } assert ( specs["dfe.funded_childcare.universal_only_children"].metadata[ @@ -537,9 +561,9 @@ def test_uk_target_reference_membership_report_is_packaged() -> None: assert membership["target_period"] == 2025 assert membership["active_reference_count"] == ACTIVE_REFERENCE_COUNT assert membership["status_counts"] == { - "active": 415, + "active": 417, "no_fact_at_or_before_period": 7, - "signed_excluded": 8, + "signed_excluded": 6, } assert membership["genuine_sum_residue"] assert membership["uprating_holds"] diff --git a/tools/generate_uk_target_references.py b/tools/generate_uk_target_references.py index 0b90e9a1c..f2908e80d 100644 --- a/tools/generate_uk_target_references.py +++ b/tools/generate_uk_target_references.py @@ -185,13 +185,23 @@ def _registry_inverse(contract: Mapping[str, Any]) -> dict[str, list[str]]: def _geography_pins(contract: Mapping[str, Any]) -> dict[str, dict[str, str]]: - return { - str(target["target_id"]): { - "geography_level": "country", + """Pin every national contract target to a Ledger geography. + + A target whose only declared level is ``region`` (the DfT London bus + rows) pins at region level with its E12 code, so it can match the + region-stamped publisher fact instead of a country row that never + exists. Every other national target pins at country level. + """ + + pins: dict[str, dict[str, str]] = {} + for target in contract.get("targets", ()): + levels = tuple(str(level) for level in target.get("geography_levels") or ()) + level = "region" if levels == ("region",) else "country" + pins[str(target["target_id"])] = { + "geography_level": level, "geography_id": _geography_id_for_target(target), } - for target in contract.get("targets", ()) - } + return pins SCOTGOV_COUNCIL_TAX_STOCK_PREFIX = "scotgov.council_tax_stock." @@ -220,6 +230,14 @@ def _geography_pins(contract: Mapping[str, Any]) -> dict[str, dict[str, str]]: # needs a per-nation redesign before it can activate. ("slc.support.", "england"), ("dfe.", "england"), + # ORR rail industry finance is Great Britain (K03000001): NI Railways sits + # outside the ORR series, and Chronicle stamps every ORR fact GB. + ("orr.", "great_britain"), + # Devolved bus publishers stamp their nation and name it in neither the + # target id nor the concept. + ("welshgov.", "wales"), + ("nithc.", "northern_ireland"), + ("dfi_ni.", "northern_ireland"), ) # DfT BUS05i rows name their area in the selector; the geography follows the # declared area, never a prefix, so a London or UK row can never be stamped From 30c7a3e188412259e0ba77ba75062cba2c8e2a15 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mar=C3=ADa=20Juaristi?= <127882282+juaristi22@users.noreply.github.com> Date: Fri, 11 Sep 2026 13:04:26 +0200 Subject: [PATCH 04/10] Align DfT bus fare receipts to the calibration year with the BUS0415 index (#890 C) BUS05ai receipts are published for the year ending March; the calibration year is a calendar year. The national compile now transports each dft_local_bus fare-receipts row by the ratio of the mean BUS0415 local bus fares index at the four quarter-ends inside the calibration calendar year to the mean at the four quarter-ends inside the receipts' April-to-March fiscal year, per publisher series (England E92000001, London E12000007). On the pinned feed that is x1.05696 for England (GBP 3,417.4m -> 3,612.0m) and x1.0 for London, whose fares were frozen. Net support is never aligned (#789 scope item 5). A missing quarter-end refuses the reference rather than guessing. The factor is declared on the spec metadata with the index concept, the series geography, the months used, their source record ids, the two means, the pre-alignment value and the adjudication (calendar-year basis, ruling 2026-09-10), so run manifests and receipts show the publisher facts the transport stands on. The compile-parity receipts move by the same factors. Co-Authored-By: Claude Fable 5.1 --- ...ity_incumbent_2025_signed_differences.json | 2 +- ...ty_production_2023_signed_differences.json | 4 +- .../build/uk_runtime/ledger_targets.py | 142 ++++++++++++++ .../tests/test_uk_bus_targets.py | 181 ++++++++++++++++++ 4 files changed, 326 insertions(+), 3 deletions(-) diff --git a/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_incumbent_2025_signed_differences.json b/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_incumbent_2025_signed_differences.json index 9acb3f522..3ffbe1ea7 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_incumbent_2025_signed_differences.json +++ b/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_incumbent_2025_signed_differences.json @@ -1906,7 +1906,7 @@ }, { "kind": "ledger_only", - "ledger_value": 3417388656.43538, + "ledger_value": 3612036036.2194014, "name": "dft.bus_fare_receipts.england", "period": 2025, "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." diff --git a/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_production_2023_signed_differences.json b/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_production_2023_signed_differences.json index 6860a29fe..7acca7d2a 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_production_2023_signed_differences.json +++ b/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_production_2023_signed_differences.json @@ -898,14 +898,14 @@ }, { "kind": "ledger_only", - "ledger_value": 3278976692.6359496, + "ledger_value": 3258254371.1953297, "name": "dft.bus_fare_receipts.england", "period": 2023, "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." }, { "kind": "ledger_only", - "ledger_value": 1342087642.33642, + "ledger_value": 1338986541.2373116, "name": "dft.bus_fare_receipts.london", "period": 2023, "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/ledger_targets.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/ledger_targets.py index 7f44fad72..6b325b40e 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/ledger_targets.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/ledger_targets.py @@ -262,6 +262,9 @@ def compile_uk_target_registry( country="uk", ) registry = _assert_region_facts_resolved_at_region(restamped, registry) + registry = align_dft_bus_fare_receipts_to_period( + restamped, registry, fact_rows, target_period=target_period + ) registry = validate_uc_source_month_coverage( restamped, registry, candidate_facts ) @@ -290,6 +293,145 @@ def compile_uk_target_registry( ) +#: DfT BUS05ai fare receipts are aligned from the publisher's year-ending-March +#: period to the calibration year with the BUS0415 local bus fares index (a +#: ruling of 2026-09-10: calendar-year basis). Net support is never aligned. +UK_DFT_BUS_FARE_RECEIPTS_CONCEPT = "dft.local_bus_passenger_fare_receipts" +UK_DFT_BUS_FARES_INDEX_CONCEPT = "dft.local_bus_fares_index" +UK_DFT_BUS_FARE_INDEX_BASIS = ( + "mean of the quarter-end BUS0415 index (March, June, September, December) " + "inside the calibration calendar year over the mean of the four quarter-ends " + "inside the receipts' April-to-March fiscal year" +) + + +def _fares_index_series( + facts: Iterable[Mapping[str, Any]], geography_id: str +) -> dict[str, tuple[float, str]]: + """Month -> (index value, source record id) for one BUS0415 series.""" + + series: dict[str, tuple[float, str]] = {} + for fact in facts: + observed = fact.get("observed_measure") + concept = ( + observed.get("source_concept") if isinstance(observed, Mapping) else None + ) + if concept != UK_DFT_BUS_FARES_INDEX_CONCEPT: + alignment = fact.get("concept_alignment") + if not isinstance(alignment, Mapping) or ( + alignment.get("canonical_concept") != UK_DFT_BUS_FARES_INDEX_CONCEPT + ): + continue + geography = fact.get("geography") + if not isinstance(geography, Mapping) or geography.get("id") != geography_id: + continue + period = fact.get("period") + if not isinstance(period, Mapping) or period.get("type") != "month": + continue + month = str(period.get("value")) + value = fact.get("value") + if not isinstance(value, (int, float)) or isinstance(value, bool): + continue + lineage = fact.get("lineage") + record_id = ( + str(lineage.get("source_record_id")) if isinstance(lineage, Mapping) else "" + ) + if month in series and series[month][0] != float(value): + raise ValueError( + f"BUS0415 series {geography_id!r} carries two values for {month!r}." + ) + series[month] = (float(value), record_id) + return series + + +def _quarter_end_months(start_year: int, start_month: int) -> tuple[str, ...]: + months = [] + year, month = start_year, start_month + for _ in range(4): + # advance to the next quarter-end month (3, 6, 9, 12) on or after + # (year, month), then step past it + while month not in (3, 6, 9, 12): + month += 1 + if month > 12: + month, year = 1, year + 1 + months.append(f"{year:04d}-{month:02d}") + month += 1 + if month > 12: + month, year = 1, year + 1 + return tuple(months) + + +def align_dft_bus_fare_receipts_to_period( + reference: LedgerTargetReference, + registry: TargetRegistry, + facts: Iterable[Mapping[str, Any]], + *, + target_period: int, +) -> TargetRegistry: + """Transport BUS05ai fare receipts to the calibration year with BUS0415. + + Applies only to ``dft_local_bus`` fare-receipt references that carry the + generator's uprating hold (fact period earlier than the target period). + Net support rows pass through untouched. The factor is declared on the + spec's metadata with the index concept, the series geography, the months + used, their source record ids and the pre-alignment value, so the run + manifest and receipts show the publisher facts the transport stands on. + """ + + if reference.family != "dft_local_bus": + return registry + if str(reference.ledger_selector.get("source_concept") or "") != ( + UK_DFT_BUS_FARE_RECEIPTS_CONCEPT + ): + return registry + if reference.uprating_from_period is None or reference.uprating_to_period is None: + return registry + fact_list = list(facts) + aligned = [] + for spec in registry.specs: + geography_id = str(spec.metadata.get("ledger_geography_id") or "") + fact_period = str(spec.metadata.get("ledger_fact_period") or "") + if not geography_id or not fact_period.isdigit(): + raise ValueError( + f"UK target {spec.name!r}: cannot align fare receipts without a " + "resolved Ledger geography and fiscal start year." + ) + start_year = int(fact_period) + from_months = _quarter_end_months(start_year, 4) + to_months = _quarter_end_months(int(target_period), 1) + series = _fares_index_series(fact_list, geography_id) + missing = [m for m in (*from_months, *to_months) if m not in series] + if missing: + raise ValueError( + f"UK target {spec.name!r}: BUS0415 series {geography_id!r} lacks " + f"quarter-end month(s) {missing}; refusing to align fare receipts." + ) + from_mean = sum(series[m][0] for m in from_months) / len(from_months) + to_mean = sum(series[m][0] for m in to_months) / len(to_months) + if from_mean <= 0 or to_mean <= 0: + raise ValueError( + f"UK target {spec.name!r}: BUS0415 index means must be positive." + ) + factor = to_mean / from_mean + record_ids = ",".join(series[m][1] for m in (*from_months, *to_months)) + metadata = { + **spec.metadata, + "uprating_index": UK_DFT_BUS_FARES_INDEX_CONCEPT, + "uprating_index_basis": UK_DFT_BUS_FARE_INDEX_BASIS, + "uprating_index_series_geography_id": geography_id, + "uprating_index_from_months": ",".join(from_months), + "uprating_index_to_months": ",".join(to_months), + "uprating_index_from_mean": f"{from_mean:.15g}", + "uprating_index_to_mean": f"{to_mean:.15g}", + "uprating_index_source_record_ids": record_ids, + "uprating_factor": f"{factor:.15g}", + "ledger_value_before_alignment": f"{spec.value:.15g}", + "uprating_adjudication": "microcosm#890 (ruling 2026-09-10: calendar-year basis)", + } + aligned.append(replace(spec, value=spec.value * factor, metadata=metadata)) + return TargetRegistry(aligned, country="uk") + + #: National references may pin a region only from the published English #: region roster; the codes are the ladder's own (geography_ladder), so no #: second register carries them. diff --git a/packages/microcosm-build/tests/test_uk_bus_targets.py b/packages/microcosm-build/tests/test_uk_bus_targets.py index 33d4969f7..6534b8eee 100644 --- a/packages/microcosm-build/tests/test_uk_bus_targets.py +++ b/packages/microcosm-build/tests/test_uk_bus_targets.py @@ -174,3 +174,184 @@ def test_compiler_refuses_region_pins_outside_the_english_region_roster() -> Non ) with pytest.raises(ValueError, match="not in the English region roster"): _assert_national_region_pin(bad) + + +def _fare_fact( + concept: str, geography: dict, period: dict, value: float, **extra +) -> dict: + return { + "aggregate_fact_key": f"ledger.aggregate_fact.v2:{concept}:{geography['id']}:{period['value']}", + "semantic_fact_key": f"ledger.semantic_fact.v2:{concept}:{geography['id']}:{period['value']}", + "assertion": "observation", + "aggregation": {"method": "sum"}, + "dimensions": {}, + "entity": { + "name": "institutional_sector", + "role": "local_bus_service_operators", + }, + "geography": geography, + "layout": {"groupby_value_id": extra.get("groupby", "england")}, + "lineage": { + "source_record_id": f"probe.{concept}.{geography['id']}.{period['value']}" + }, + "observed_measure": { + "source_concept": concept, + "source_name": "dft", + "source_measure_id": concept.rsplit(".", 1)[-1], + "unit": extra.get("unit", "gbp"), + }, + "period": period, + "period_coverage": extra.get("coverage", {}), + "source": {"source_name": "dft", "source_sha256": "0" * 64}, + "value": value, + } + + +def _index_facts(geography: dict, values: dict[str, float]) -> list[dict]: + return [ + _fare_fact( + "dft.local_bus_fares_index", + geography, + {"type": "month", "value": month}, + value, + unit="index_2005_100", + ) + for month, value in values.items() + ] + + +def test_quarter_end_months_follow_the_fiscal_and_calendar_windows() -> None: + from microcosm.build.uk_runtime.ledger_targets import _quarter_end_months + + assert _quarter_end_months(2024, 4) == ("2024-06", "2024-09", "2024-12", "2025-03") + assert _quarter_end_months(2025, 1) == ("2025-03", "2025-06", "2025-09", "2025-12") + + +def test_fare_receipts_align_with_the_bus0415_series_and_support_does_not() -> None: + from microcosm.build.ledger_targets import ( + LedgerTargetReference, + compile_ledger_target_references, + ) + from microcosm.build.uk_runtime.ledger_targets import ( + align_dft_bus_fare_receipts_to_period, + ) + + england = {"id": "E92000001", "level": "country", "vintage": "current"} + coverage = {"start_date": "2024-04-01", "end_date": "2025-03-31", "basis": "fiscal"} + fare = _fare_fact( + "dft.local_bus_passenger_fare_receipts", + england, + {"type": "fiscal_year", "value": 2025}, + 1_000.0, + coverage=coverage, + ) + support = _fare_fact( + "dft.local_bus_total_estimated_net_support", + england, + {"type": "fiscal_year", "value": 2025}, + 500.0, + coverage=coverage, + ) + index = _index_facts( + england, + { + "2024-03": 100.0, + "2024-06": 100.0, + "2024-09": 100.0, + "2024-12": 100.0, + "2025-03": 120.0, + "2025-06": 120.0, + "2025-09": 120.0, + "2025-12": 120.0, + }, + ) + + def reference(name: str, concept: str) -> LedgerTargetReference: + return LedgerTargetReference( + name=name, + ledger_selector={ + "source_name": "dft", + "source_concept": concept, + "period_type": "fiscal_year", + "geography_level": "country", + "geography_id": "E92000001", + }, + entity="household", + measure=name, + family="dft_local_bus", + period=2025, + uprating_from_period="2024", + uprating_to_period=2025, + ) + + fare_reference = reference("fares", "dft.local_bus_passenger_fare_receipts") + support_reference = reference( + "support", "dft.local_bus_total_estimated_net_support" + ) + facts = [fare, support, *index] + + fares = compile_ledger_target_references([fare], [fare_reference], country="uk") + aligned = align_dft_bus_fare_receipts_to_period( + fare_reference, fares, facts, target_period=2025 + ) + (spec,) = aligned.specs + # from-window mean = (100+100+100+120)/4 = 105; to-window mean = 120 + assert spec.value == pytest.approx(1_000.0 * 120.0 / 105.0) + assert spec.metadata["uprating_index"] == "dft.local_bus_fares_index" + assert ( + spec.metadata["uprating_index_from_months"] == "2024-06,2024-09,2024-12,2025-03" + ) + assert ( + spec.metadata["uprating_index_to_months"] == "2025-03,2025-06,2025-09,2025-12" + ) + assert spec.metadata["ledger_value_before_alignment"] == "1000" + assert spec.metadata["uprating_factor"] == f"{120.0 / 105.0:.15g}" + assert ( + "probe.dft.local_bus_fares_index.E92000001.2025-12" + in (spec.metadata["uprating_index_source_record_ids"]) + ) + + supports = compile_ledger_target_references( + [support], [support_reference], country="uk" + ) + untouched = align_dft_bus_fare_receipts_to_period( + support_reference, supports, facts, target_period=2025 + ) + assert untouched.specs[0].value == 500.0 + assert "uprating_factor" not in untouched.specs[0].metadata + + with pytest.raises(ValueError, match="lacks quarter-end month"): + align_dft_bus_fare_receipts_to_period( + fare_reference, fares, [fare, *index[:-1]], target_period=2025 + ) + + +def test_bus0415_alignment_on_the_pinned_feed() -> None: + root = Path(__file__).resolve().parents[3] + feed = root / ".codex-work/uk-artifact" + if not feed.is_dir(): + pytest.skip("pinned UK Chronicle national artifact directory is not present") + from microcosm.build.ledger_artifact import load_ledger_consumer_artifact + from microcosm.build.uk_runtime.ledger_targets import compile_uk_target_registry + from microcosm.build.uk_runtime.national_chronicle_feed import ( + load_uk_national_chronicle_feed, + ) + + pin = load_uk_national_chronicle_feed() + artifact = load_ledger_consumer_artifact( + feed, + expected_facts_sha256=pin.facts_sha256, + expected_manifest_sha256=pin.manifest_sha256, + ) + compiled = compile_uk_target_registry(artifact.facts, target_period=2025) + specs = {spec.name: spec for spec in compiled.registry.specs} + england = specs["dft.bus_fare_receipts.england"] + london = specs["dft.bus_fare_receipts.london"] + assert float(england.metadata["uprating_factor"]) == pytest.approx( + 204.125 / 193.125 + ) + assert england.value == pytest.approx(3_417_388_656.43538 * 204.125 / 193.125) + assert float(london.metadata["uprating_factor"]) == pytest.approx(1.0) + assert london.value == pytest.approx(1_347_434_943.01459) + assert "uprating_factor" not in specs["dft.bus_net_support.england"].metadata + assert "uprating_factor" not in specs["dft.bus_net_support.london"].metadata From b923349251f628ead56a905db6857c726518fede Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mar=C3=ADa=20Juaristi?= <127882282+juaristi22@users.noreply.github.com> Date: Fri, 11 Sep 2026 13:35:30 +0200 Subject: [PATCH 05/10] Bind the OBR cars fuel duty, ORR rail support, ONS energy spend and devolved bus legs (#890 E1, G, F1) Eight net-new national targets, all pinned Chronicle facts, in registry scope with their no-incumbent-ancestor declarations: - obr.fuel_duties_cars binds the OBR fuel duty receipts from cars (FY2025 projection GBP 15.9bn) on the engine's cars-only fuel_duty. The all-road-users obr.fuel_duties row is signed out of the reference surface and its #757 calibration measure exclusion retires with it. - orr.rail_government_support binds ORR table 7270 total government support (FY2024 GBP 21.6bn, Great Britain, so Northern Ireland households are filtered out of the measure) on rail_subsidy_spending, replacing the incumbent's post-calibration rail scaler. - ons.household_energy_expenditure binds ONS Consumer Trends COICOP 04.5 (CY2025 GBP 43.4bn) on domestic_energy_consumption, with the other-fuels scope difference noted. - The devolved local-bus legs: Scotland passenger revenue (GBP 391m) and all government support (GBP 499m) from the 2025 Scottish Transport Statistics; Wales public support through local authorities (concessionary fares plus support to operators, GBP 131.5m gross) from the StatsWales outturn; Northern Ireland Translink bus receipts (GBP 150.1m, Ulsterbus plus Metro/Glider) from DfI; and Northern Ireland public transport support as a joint bus-plus-rail cell (PSO plus concessionary compensation, GBP 111.7m) from the NITHC accounts, measured on bus_subsidy_spending + rail_subsidy_spending. The UK net-support row is signed out as covered by the four country legs; the UK fares row stays out because Wales publishes no fare revenue. Active references 417 -> 424; contract targets 236 -> 244 (registry scope 202 -> 210, unmapped declarations 19 -> 27). Compile-parity receipts gain the new rows as ledger-only entries. Co-Authored-By: Claude Fable 5.1 --- .../uk/calibration_measure_exclusions.json | 17 +- ...ity_incumbent_2025_signed_differences.json | 71 +++- ...ty_production_2023_signed_differences.json | 55 ++- .../build/uk/target_reference_membership.json | 208 +++++++++- .../target_reference_signed_exclusions.json | 16 +- .../microcosm/build/uk/target_references.json | 227 ++++++++++- .../build/uk/uk_population_targets.json | 369 +++++++++++++++++- .../tests/test_uk_bus_targets.py | 1 + .../tests/test_uk_measure_simulation.py | 12 +- .../tests/test_uk_national_calibration.py | 2 +- .../tests/test_uk_population_targets.py | 14 +- .../tests/test_uk_target_references.py | 42 +- 12 files changed, 961 insertions(+), 73 deletions(-) diff --git a/packages/microcosm-build/src/microcosm/build/uk/calibration_measure_exclusions.json b/packages/microcosm-build/src/microcosm/build/uk/calibration_measure_exclusions.json index 3ce5d4cfd..b2403bf81 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/calibration_measure_exclusions.json +++ b/packages/microcosm-build/src/microcosm/build/uk/calibration_measure_exclusions.json @@ -388,18 +388,9 @@ "approved_on": "2026-08-26", "expires_on": "2026-11-26" }, - { - "name": "obr.fuel_duties", - "reason": "Universe scope: the OBR fact is all-road-users receipts (including freight and business mileage) against a household frame, compounded by LCFS diary under-capture at 37% of implied litres; the binding is verified correct and the gap is scope, not measurement. The household-incidence-vs-total-receipts ledger retarget question is pending chronicle-side adjudication.", - "tracking": "microcosm#757", - "approved_by": "juaristi22", - "adjudication": "microcosm#757 (the uk_target_fit dispositions, issue comment 5427936411)", - "approved_on": "2026-08-26", - "expires_on": "2026-11-26" - }, { "name": "ons.savings_interest_income", - "reason": "The spine carries £13.8bn of savings interest income against the ONS £95.5bn (-86% at initialization on spine-m, -89% after the solve): the FRS interest income capture, not the weights. Unreachable by reweighting within the stretch bound: a frame-side gap, not a weight question; while bound it pulls the joint solve toward weight concentration (housing benefit alone triples the max/median ratio under grain_equal). One-month window: correct the spine or re-adjudicate.", + "reason": "The spine carries \u00a313.8bn of savings interest income against the ONS \u00a395.5bn (-86% at initialization on spine-m, -89% after the solve): the FRS interest income capture, not the weights. Unreachable by reweighting within the stretch bound: a frame-side gap, not a weight question; while bound it pulls the joint solve toward weight concentration (housing benefit alone triples the max/median ratio under grain_equal). One-month window: correct the spine or re-adjudicate.", "tracking": "microcosm#866", "approved_by": "juaristi22", "adjudication": "microcosm#762 (A16, ruling 2026-09-03; receipts R8-R10 in experiments/762-uk-rowwise-candidate-receipts.md)", @@ -408,7 +399,7 @@ }, { "name": "obr.housing_benefit", - "reason": "The spine carries £2.7bn of housing benefit against the OBR £12.1bn (-78% at initialization on spine-m). Under grain_equal the solver chases the aggregate by multiplying the few HB households (£9.4bn at 512 epochs, £11.4bn at 1500), which halves the effective sample and doubles Glasgow's social-rent count; under uniform it is ignored. Unreachable by reweighting within the stretch bound: a frame-side gap, not a weight question; while bound it pulls the joint solve toward weight concentration (housing benefit alone triples the max/median ratio under grain_equal). One-month window: correct the spine or re-adjudicate.", + "reason": "The spine carries \u00a32.7bn of housing benefit against the OBR \u00a312.1bn (-78% at initialization on spine-m). Under grain_equal the solver chases the aggregate by multiplying the few HB households (\u00a39.4bn at 512 epochs, \u00a311.4bn at 1500), which halves the effective sample and doubles Glasgow's social-rent count; under uniform it is ignored. Unreachable by reweighting within the stretch bound: a frame-side gap, not a weight question; while bound it pulls the joint solve toward weight concentration (housing benefit alone triples the max/median ratio under grain_equal). One-month window: correct the spine or re-adjudicate.", "tracking": "microcosm#867", "approved_by": "juaristi22", "adjudication": "microcosm#762 (A16, ruling 2026-09-03; receipts R8-R10 in experiments/762-uk-rowwise-candidate-receipts.md)", @@ -447,7 +438,7 @@ "reason": "Zero-support channel under the corrected UC composition proxy: since #879 the payment-distribution rows are measured on the relationship-based uc_calibration_family_type, and no benefit unit in the spine is a couple without children with an annual UC award in this band (final estimate 0.0, -100% on #879's corrected comparison; no pool carrier on the K=15 rowwise clone of spine-p either, where the #355 size selection refuses the row by name). The dense seam carries the row as a capped miss; a size selection cannot carry it. The band catalogue and the unbounded top band are tracked in microcosm#736; the sibling 26,400-27,600 band already sits in this register.", "tracking": "microcosm#736", "approved_by": "juaristi22", - "adjudication": "microcosm#355 (dataset-size experiment; ruling by María 2026-09-08 after the 45,800 smoke's by-name refusal)", + "adjudication": "microcosm#355 (dataset-size experiment; ruling by Mar\u00eda 2026-09-08 after the 45,800 smoke's by-name refusal)", "approved_on": "2026-09-08", "expires_on": "2026-12-08" }, @@ -456,7 +447,7 @@ "reason": "Zero-support channel under the corrected UC composition proxy: since #879 the payment-distribution rows are measured on the relationship-based uc_calibration_family_type, and no benefit unit in the spine is a couple without children with an annual UC award in this band (final estimate 0.0, -100% on #879's corrected comparison; no pool carrier on the K=15 rowwise clone of spine-p either, where the #355 size selection refuses the row by name). The dense seam carries the row as a capped miss; a size selection cannot carry it. The band catalogue and the unbounded top band are tracked in microcosm#736; the sibling 26,400-27,600 band already sits in this register.", "tracking": "microcosm#736", "approved_by": "juaristi22", - "adjudication": "microcosm#355 (dataset-size experiment; ruling by María 2026-09-08 after the 45,800 smoke's by-name refusal)", + "adjudication": "microcosm#355 (dataset-size experiment; ruling by Mar\u00eda 2026-09-08 after the 45,800 smoke's by-name refusal)", "approved_on": "2026-09-08", "expires_on": "2026-12-08" } diff --git a/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_incumbent_2025_signed_differences.json b/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_incumbent_2025_signed_differences.json index 3ffbe1ea7..266553cfd 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_incumbent_2025_signed_differences.json +++ b/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_incumbent_2025_signed_differences.json @@ -1,11 +1,11 @@ { - "compiled_count": 417, + "compiled_count": 424, "counts_by_kind": { "calibration_drift": 172, - "fixture_only": 268, - "ledger_only": 48 + "fixture_only": 269, + "ledger_only": 56 }, - "difference_count": 488, + "difference_count": 497, "differences": [ { "fixture_value": 1608000.0, @@ -357,6 +357,13 @@ "period": 2025, "reason": "Fixture row has no Ledger-compiled counterpart at this comparison period." }, + { + "fixture_value": 24241874775.213375, + "kind": "fixture_only", + "name": "obr.fuel_duties", + "period": 2025, + "reason": "Fixture row has no Ledger-compiled counterpart at this comparison period." + }, { "fixture_value": 82000.0, "kind": "fixture_only", @@ -1904,6 +1911,13 @@ "period": 2025, "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." }, + { + "kind": "ledger_only", + "ledger_value": 150082817.48999995, + "name": "dfi_ni.bus.passenger_receipts", + "period": 2025, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, { "kind": "ledger_only", "ledger_value": 3612036036.2194014, @@ -2079,6 +2093,27 @@ "period": 2025, "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." }, + { + "kind": "ledger_only", + "ledger_value": 111700000.0, + "name": "nithc.public_transport_support", + "period": 2025, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, + { + "kind": "ledger_only", + "ledger_value": 15900000000.0, + "name": "obr.fuel_duties_cars", + "period": 2025, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, + { + "kind": "ledger_only", + "ledger_value": 43427000000.0, + "name": "ons.household_energy_expenditure", + "period": 2025, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, { "kind": "ledger_only", "ledger_value": 7553013.0, @@ -2156,6 +2191,27 @@ "period": 2025, "reason": "María ruling 2026-08-21: new male 90_plus tail constrains ages 90+; the incumbent six-year terminal band left ages 91+ unconstrained." }, + { + "kind": "ledger_only", + "ledger_value": 21621100549.34064, + "name": "orr.rail_government_support", + "period": 2025, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, + { + "kind": "ledger_only", + "ledger_value": 499000000.0, + "name": "scotgov.bus.government_support", + "period": 2025, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, + { + "kind": "ledger_only", + "ledger_value": 391000000.0, + "name": "scotgov.bus.passenger_revenue", + "period": 2025, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, { "kind": "ledger_only", "ledger_value": 6103320.0, @@ -2219,6 +2275,13 @@ "period": 2025, "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." }, + { + "kind": "ledger_only", + "ledger_value": 131489970.0, + "name": "welshgov.bus.public_support", + "period": 2025, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, { "fixture_value": 999000.0, "kind": "calibration_drift", diff --git a/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_production_2023_signed_differences.json b/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_production_2023_signed_differences.json index 7acca7d2a..d4f979495 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_production_2023_signed_differences.json +++ b/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_production_2023_signed_differences.json @@ -1,11 +1,11 @@ { - "compiled_count": 232, + "compiled_count": 239, "counts_by_kind": { "calibration_drift": 12, "fixture_only": 125, - "ledger_only": 208 + "ledger_only": 215 }, - "difference_count": 345, + "difference_count": 352, "differences": [ { "fixture_value": 82000.0, @@ -896,6 +896,13 @@ "period": 2023, "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." }, + { + "kind": "ledger_only", + "ledger_value": 135820000.0, + "name": "dfi_ni.bus.passenger_receipts", + "period": 2023, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, { "kind": "ledger_only", "ledger_value": 3258254371.1953297, @@ -2002,6 +2009,27 @@ "period": 2023, "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." }, + { + "kind": "ledger_only", + "ledger_value": 99200000.0, + "name": "nithc.public_transport_support", + "period": 2023, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, + { + "kind": "ledger_only", + "ledger_value": 14200000000.0, + "name": "obr.fuel_duties_cars", + "period": 2023, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, + { + "kind": "ledger_only", + "ledger_value": 52505000000.0, + "name": "ons.household_energy_expenditure", + "period": 2023, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, { "kind": "ledger_only", "ledger_value": 7497928.0, @@ -2338,6 +2366,27 @@ "period": 2023, "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." }, + { + "kind": "ledger_only", + "ledger_value": 23120884840.50285, + "name": "orr.rail_government_support", + "period": 2023, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, + { + "kind": "ledger_only", + "ledger_value": 438893000.0, + "name": "scotgov.bus.government_support", + "period": 2023, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, + { + "kind": "ledger_only", + "ledger_value": 97426240.0, + "name": "welshgov.bus.public_support", + "period": 2023, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, { "fixture_value": 557000.0, "kind": "calibration_drift", diff --git a/packages/microcosm-build/src/microcosm/build/uk/target_reference_membership.json b/packages/microcosm-build/src/microcosm/build/uk/target_reference_membership.json index 47adb1575..771dbabbe 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/target_reference_membership.json +++ b/packages/microcosm-build/src/microcosm/build/uk/target_reference_membership.json @@ -1,13 +1,13 @@ { "source_fact_feed": "chronicle-uk-c6f93614-consumer-facts", "target_period": 2025, - "candidate_count": 430, - "contract_target_count": 202, - "active_reference_count": 417, + "candidate_count": 438, + "contract_target_count": 210, + "active_reference_count": 424, "status_counts": { - "active": 417, + "active": 424, "no_fact_at_or_before_period": 7, - "signed_excluded": 6 + "signed_excluded": 7 }, "geography_pins": { "obr.income_tax": { @@ -817,10 +817,44 @@ "dft.bus_net_support.uk": { "geography_level": "country", "geography_id": "K02000001" + }, + "obr.fuel_duties_cars": { + "geography_level": "country", + "geography_id": "K02000001" + }, + "orr.rail_government_support": { + "geography_level": "country", + "geography_id": "K03000001" + }, + "ons.household_energy_expenditure": { + "geography_level": "country", + "geography_id": "K02000001" + }, + "scotgov.bus.passenger_revenue": { + "geography_level": "country", + "geography_id": "S92000003" + }, + "scotgov.bus.government_support": { + "geography_level": "country", + "geography_id": "S92000003" + }, + "welshgov.bus.public_support": { + "geography_level": "country", + "geography_id": "W92000004" + }, + "dfi_ni.bus.passenger_receipts": { + "geography_level": "country", + "geography_id": "N92000002" + }, + "nithc.public_transport_support": { + "geography_level": "country", + "geography_id": "N92000002" } }, "genuine_sum_residue": [ "dfe.funded_childcare.working_parent_children_2_to_4", + "dfi_ni.bus.passenger_receipts", + "nithc.public_transport_support", "obr.esa", "ons.population.age_0_9_by_region", "ons.population.age_10_19_by_region", @@ -876,7 +910,8 @@ "slc.repayments.england_plan_2", "slc.repayments.england_plan_5", "slc.repayments.england_postgraduate", - "slc.repayments.england_total_higher_education" + "slc.repayments.england_total_higher_education", + "welshgov.bus.public_support" ], "uprating_holds": [ { @@ -2693,6 +2728,36 @@ "name": "dft.bus_net_support.london", "from": "2024", "to": "2025" + }, + { + "name": "orr.rail_government_support", + "from": "2024", + "to": "2025" + }, + { + "name": "scotgov.bus.passenger_revenue", + "from": "2024", + "to": "2025" + }, + { + "name": "scotgov.bus.government_support", + "from": "2024", + "to": "2025" + }, + { + "name": "welshgov.bus.public_support", + "from": "2024", + "to": "2025" + }, + { + "name": "dfi_ni.bus.passenger_receipts", + "from": "2024", + "to": "2025" + }, + { + "name": "nithc.public_transport_support", + "from": "2024", + "to": "2025" } ], "targets": { @@ -2787,17 +2852,14 @@ ] }, "obr.fuel_duties": { - "status": "active", + "status": "signed_excluded", "candidates": [ { "name": "obr.fuel_duties", - "status": "active", + "status": "signed_excluded", "matched_fact_count_overall": 7, "matched_fact_count_at_or_before_period": 2, - "resolved_period": 2025, - "resolved_value": 24241874775.213375, - "resolved_fact_period": "2025", - "resolved_fact_key": "ledger_aggregate_fact_v2_9273c0c9f7045493ed2e984a" + "signed_rationale": "The OBR fuel duties fact is all-road-users receipts (freight, vans, business mileage) against a household frame; obr.fuel_duties_cars binds the cars receipts from the OBR vehicle-type release instead (ruling 2026-09-10). The all-road-users row is retained in the contract for registry parity and signed out here rather than carried as a calibration measure exclusion." } ] }, @@ -7973,7 +8035,127 @@ "status": "signed_excluded", "matched_fact_count_overall": 0, "matched_fact_count_at_or_before_period": 0, - "signed_rationale": "BUS05i publishes no UK total and Microcosm declares no method to construct one." + "signed_rationale": "BUS05i publishes no UK total; net support is now bound as four country legs (DfT England, Scottish Transport Statistics, StatsWales outturn, NITHC accounts), so a UK row would double-bind them and no England-to-UK uplift is declared." + } + ] + }, + "obr.fuel_duties_cars": { + "status": "active", + "candidates": [ + { + "name": "obr.fuel_duties_cars", + "status": "active", + "matched_fact_count_overall": 7, + "matched_fact_count_at_or_before_period": 4, + "resolved_period": 2025, + "resolved_value": 15900000000.0, + "resolved_fact_period": "2025", + "resolved_fact_key": "ledger_aggregate_fact_v2_e67ab63ef45a3912ba9524e2" + } + ] + }, + "orr.rail_government_support": { + "status": "active", + "candidates": [ + { + "name": "orr.rail_government_support", + "status": "active", + "matched_fact_count_overall": 10, + "matched_fact_count_at_or_before_period": 10, + "resolved_period": 2025, + "resolved_value": 21621100549.34064, + "resolved_fact_period": "2024", + "resolved_fact_key": "ledger_aggregate_fact_v2_b16b9b3f2329455f907f9ccc" + } + ] + }, + "ons.household_energy_expenditure": { + "status": "active", + "candidates": [ + { + "name": "ons.household_energy_expenditure", + "status": "active", + "matched_fact_count_overall": 6, + "matched_fact_count_at_or_before_period": 6, + "resolved_period": 2025, + "resolved_value": 43427000000.0, + "resolved_fact_period": "2025", + "resolved_fact_key": "ledger_aggregate_fact_v2_a139145f4db5fea0260908f8" + } + ] + }, + "scotgov.bus.passenger_revenue": { + "status": "active", + "candidates": [ + { + "name": "scotgov.bus.passenger_revenue", + "status": "active", + "matched_fact_count_overall": 1, + "matched_fact_count_at_or_before_period": 1, + "resolved_period": 2025, + "resolved_value": 391000000.0, + "resolved_fact_period": "2024", + "resolved_fact_key": "ledger_aggregate_fact_v2_c4269bb83534f1e46f5801c1" + } + ] + }, + "scotgov.bus.government_support": { + "status": "active", + "candidates": [ + { + "name": "scotgov.bus.government_support", + "status": "active", + "matched_fact_count_overall": 2, + "matched_fact_count_at_or_before_period": 2, + "resolved_period": 2025, + "resolved_value": 499000000.0, + "resolved_fact_period": "2024", + "resolved_fact_key": "ledger_aggregate_fact_v2_ceb60fd41d0f5587be94c234" + } + ] + }, + "welshgov.bus.public_support": { + "status": "active", + "candidates": [ + { + "name": "welshgov.bus.public_support", + "status": "active", + "matched_fact_count_overall": 6, + "matched_fact_count_at_or_before_period": 6, + "resolved_period": 2025, + "resolved_value": 131489970.0, + "resolved_fact_period": "2024", + "resolved_fact_key": "ledger_aggregate_fact_v2_a10fcfec7cb458fa0c8c7f10" + } + ] + }, + "dfi_ni.bus.passenger_receipts": { + "status": "active", + "candidates": [ + { + "name": "dfi_ni.bus.passenger_receipts", + "status": "active", + "matched_fact_count_overall": 12, + "matched_fact_count_at_or_before_period": 12, + "resolved_period": 2025, + "resolved_value": 150082817.48999995, + "resolved_fact_period": "2024", + "resolved_fact_key": "ledger_aggregate_fact_v2_96f72989614e0c5faaeeabbb" + } + ] + }, + "nithc.public_transport_support": { + "status": "active", + "candidates": [ + { + "name": "nithc.public_transport_support", + "status": "active", + "matched_fact_count_overall": 4, + "matched_fact_count_at_or_before_period": 4, + "resolved_period": 2025, + "resolved_value": 111700000.0, + "resolved_fact_period": "2024", + "resolved_fact_key": "ledger_aggregate_fact_v2_be8eb7011b6f65fa65d52fcb" } ] } diff --git a/packages/microcosm-build/src/microcosm/build/uk/target_reference_signed_exclusions.json b/packages/microcosm-build/src/microcosm/build/uk/target_reference_signed_exclusions.json index ae052d712..70d4d2184 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/target_reference_signed_exclusions.json +++ b/packages/microcosm-build/src/microcosm/build/uk/target_reference_signed_exclusions.json @@ -43,11 +43,19 @@ }, { "target_id": "dft.bus_net_support.uk", - "reason_id": "no_publisher_uk_total", - "rationale": "BUS05i publishes no UK total and Microcosm declares no method to construct one.", - "tracking": "microcosm#789", + "reason_id": "covered_by_country_legs", + "rationale": "BUS05i publishes no UK total; net support is now bound as four country legs (DfT England, Scottish Transport Statistics, StatsWales outturn, NITHC accounts), so a UK row would double-bind them and no England-to-UK uplift is declared.", + "tracking": "microcosm#890", "approved_by": "juaristi22", - "approved_on": "2026-09-05" + "approved_on": "2026-09-11" + }, + { + "target_id": "obr.fuel_duties", + "reason_id": "universe_scope_all_road_users", + "rationale": "The OBR fuel duties fact is all-road-users receipts (freight, vans, business mileage) against a household frame; obr.fuel_duties_cars binds the cars receipts from the OBR vehicle-type release instead (ruling 2026-09-10). The all-road-users row is retained in the contract for registry parity and signed out here rather than carried as a calibration measure exclusion.", + "tracking": "microcosm#890", + "approved_by": "juaristi22", + "approved_on": "2026-09-11" } ] } diff --git a/packages/microcosm-build/src/microcosm/build/uk/target_references.json b/packages/microcosm-build/src/microcosm/build/uk/target_references.json index 93f30fa9c..5d947b470 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/target_references.json +++ b/packages/microcosm-build/src/microcosm/build/uk/target_references.json @@ -126,25 +126,6 @@ }, "assertion_policy": "allow_source_projection" }, - { - "name": "obr.fuel_duties", - "ledger_selector": { - "source_name": "obr", - "source_concept": "obr.fuel_duties", - "entity_name": "person", - "geography_level": "country", - "geography_id": "K02000001" - }, - "entity": "household", - "measure": "obr/fuel_duties", - "family": "obr", - "period": 2025, - "metadata": { - "contract_target_id": "obr.fuel_duties", - "measure_kind": "prepared_column" - }, - "assertion_policy": "allow_source_projection" - }, { "name": "hmrc.cgt.liability_total", "ledger_selector": { @@ -11205,6 +11186,214 @@ }, "uprating_from_period": "2024", "uprating_to_period": 2025 + }, + { + "name": "obr.fuel_duties_cars", + "ledger_selector": { + "source_name": "obr", + "source_concept": "obr.fuel_duty.receipts", + "entity_name": "government", + "period_type": "fiscal_year", + "dimension_values": { + "vehicle_category": "cars" + }, + "geography_level": "country", + "geography_id": "K02000001" + }, + "entity": "household", + "measure": "obr/fuel_duties_cars", + "family": "obr", + "period": 2025, + "metadata": { + "contract_target_id": "obr.fuel_duties_cars", + "measure_kind": "prepared_column" + }, + "assertion_policy": "allow_source_projection" + }, + { + "name": "orr.rail_government_support", + "ledger_selector": { + "source_name": "orr", + "source_concept": "orr.rail.total_government_support", + "entity_name": "government", + "period_type": "fiscal_year", + "geography_level": "country", + "geography_id": "K03000001" + }, + "entity": "household", + "measure": "orr/rail_government_support", + "family": "orr_rail", + "period": 2025, + "metadata": { + "contract_target_id": "orr.rail_government_support", + "measure_kind": "prepared_column", + "observation_basis": "fiscal_year_flow" + }, + "uprating_from_period": "2024", + "uprating_to_period": 2025 + }, + { + "name": "ons.household_energy_expenditure", + "ledger_selector": { + "source_name": "ons", + "source_concept": "ons.household_expenditure.electricity_gas_other_fuels", + "entity_name": "household", + "period_type": "calendar_year", + "dimension_values": { + "coicop": "04.5", + "frequency": "annual" + }, + "geography_level": "country", + "geography_id": "K02000001" + }, + "entity": "household", + "measure": "ons/household_energy_expenditure", + "family": "ons_national_accounts", + "period": 2025, + "metadata": { + "contract_target_id": "ons.household_energy_expenditure", + "measure_kind": "prepared_column" + } + }, + { + "name": "scotgov.bus.passenger_revenue", + "ledger_selector": { + "source_name": "scotgov", + "source_concept": "scotgov.bus.operator_revenue", + "layout_groupby_value_id": "passenger_revenue", + "entity_name": "institutional_sector", + "period_type": "fiscal_year", + "geography_level": "country", + "geography_id": "S92000003" + }, + "entity": "household", + "measure": "scotgov/bus_passenger_revenue", + "family": "dft_local_bus", + "period": 2025, + "metadata": { + "contract_target_id": "scotgov.bus.passenger_revenue", + "measure_kind": "prepared_column", + "observation_basis": "fiscal_year_flow" + }, + "uprating_from_period": "2024", + "uprating_to_period": 2025 + }, + { + "name": "scotgov.bus.government_support", + "ledger_selector": { + "source_name": "scotgov", + "source_concept": "scotgov.bus.government_support", + "entity_name": "government", + "period_type": "fiscal_year", + "dimension_values": { + "funding_component": "all_government_support" + }, + "geography_level": "country", + "geography_id": "S92000003" + }, + "entity": "household", + "measure": "scotgov/bus_government_support", + "family": "dft_local_bus", + "period": 2025, + "metadata": { + "contract_target_id": "scotgov.bus.government_support", + "measure_kind": "prepared_column", + "observation_basis": "fiscal_year_flow" + }, + "uprating_from_period": "2024", + "uprating_to_period": 2025 + }, + { + "name": "welshgov.bus.public_support", + "ledger_selector": { + "source_name": "welshgov", + "source_concept": "welshgov.local_transport.gross_expenditure", + "entity_name": "government", + "period_type": "fiscal_year", + "dimension_values": { + "expenditure_component": "Gross expenditure", + "service_line": [ + "Concessionary fares", + "Support to operators" + ] + }, + "geography_level": "country", + "geography_id": "W92000004" + }, + "entity": "household", + "measure": "welshgov/bus_public_support", + "family": "dft_local_bus", + "period": 2025, + "metadata": { + "contract_target_id": "welshgov.bus.public_support", + "measure_kind": "prepared_column", + "observation_basis": "fiscal_year_flow" + }, + "value_operation": "sum", + "expected_member_count": 2, + "uprating_from_period": "2024", + "uprating_to_period": 2025 + }, + { + "name": "dfi_ni.bus.passenger_receipts", + "ledger_selector": { + "source_name": "dfi_ni", + "source_concept": "dfi_ni.translink_bus.passenger_receipts", + "entity_name": "institutional_sector", + "period_type": "fiscal_year", + "dimension_values": { + "service": [ + "ulsterbus", + "metro_glider" + ] + }, + "geography_level": "country", + "geography_id": "N92000002" + }, + "entity": "household", + "measure": "dfi_ni/bus_passenger_receipts", + "family": "dft_local_bus", + "period": 2025, + "metadata": { + "contract_target_id": "dfi_ni.bus.passenger_receipts", + "measure_kind": "prepared_column", + "observation_basis": "fiscal_year_flow" + }, + "value_operation": "sum", + "expected_member_count": 2, + "uprating_from_period": "2024", + "uprating_to_period": 2025 + }, + { + "name": "nithc.public_transport_support", + "ledger_selector": { + "source_name": "nithc", + "source_concept": "nithc.dfi_transaction_value", + "entity_name": "government", + "period_type": "fiscal_year", + "dimension_values": { + "mode_scope": "all_public_transport", + "funding_component": [ + "Public Service Obligation compensation", + "Concessionary fare compensation for a range of groups" + ] + }, + "geography_level": "country", + "geography_id": "N92000002" + }, + "entity": "household", + "measure": "nithc/public_transport_support", + "family": "dft_local_bus", + "period": 2025, + "metadata": { + "contract_target_id": "nithc.public_transport_support", + "measure_kind": "prepared_column", + "observation_basis": "fiscal_year_flow" + }, + "value_operation": "sum", + "expected_member_count": 2, + "uprating_from_period": "2024", + "uprating_to_period": 2025 } ] } diff --git a/packages/microcosm-build/src/microcosm/build/uk/uk_population_targets.json b/packages/microcosm-build/src/microcosm/build/uk/uk_population_targets.json index 9b2517a39..3ec0b3eb6 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/uk_population_targets.json +++ b/packages/microcosm-build/src/microcosm/build/uk/uk_population_targets.json @@ -697,7 +697,15 @@ "dft.bus_net_support.england_outside_london": "#789 consumer target.", "dft.bus_fare_receipts.uk": "#789 consumer target.", "dft.bus_net_support.uk": "#789 consumer target.", - "hmrc.cgt.liability_total": "Observed FY2024-25 individuals liability from the pinned HMRC Table 1 fact. No incumbent same-basis liability target; OBR cash is a separate diagnostic." + "hmrc.cgt.liability_total": "Observed FY2024-25 individuals liability from the pinned HMRC Table 1 fact. No incumbent same-basis liability target; OBR cash is a separate diagnostic.", + "obr.fuel_duties_cars": "No incumbent ancestor: uk-data bound the all-road-users OBR fuel duties total; the cars receipts from the OBR vehicle-type release are a net-new household-incidence target (microcosm#890 E1, ruling 2026-09-10).", + "orr.rail_government_support": "No incumbent target: uk-data scaled rail_subsidy_spending to the ORR total after calibration (the #790 rail scaler); microcosm#890 G binds the ORR table 7270 fact instead.", + "ons.household_energy_expenditure": "No incumbent target: uk-data raked energy to NEED means and never bound a household-sector expenditure total; microcosm#890 F1 binds ONS Consumer Trends COICOP 04.5.", + "scotgov.bus.passenger_revenue": "No incumbent target: uk-data uplifted DfT England fares to the UK by population; microcosm#890 binds the Scotland leg from Scottish Transport Statistics.", + "scotgov.bus.government_support": "No incumbent target: uk-data uplifted DfT England support to the UK by population; microcosm#890 binds the Scotland leg from Scottish Transport Statistics.", + "welshgov.bus.public_support": "No incumbent target: uk-data uplifted DfT England support to the UK by population; microcosm#890 binds the Wales leg (local-authority route) from the StatsWales revenue outturn.", + "dfi_ni.bus.passenger_receipts": "No incumbent target: uk-data uplifted DfT England fares to the UK by population; microcosm#890 binds the Northern Ireland leg from DfI Public Transport Statistics.", + "nithc.public_transport_support": "No incumbent target: uk-data uplifted DfT England support to the UK by population; microcosm#890 binds Northern Ireland public transport support from the NITHC accounts as a joint bus-plus-rail cell." }, "suppressed_ancestors": { "hmrc/salary_sacrifice_it_relief_basic_rate": { @@ -931,7 +939,15 @@ "dft.bus_fare_receipts.england_outside_london", "dft.bus_net_support.england_outside_london", "dft.bus_fare_receipts.uk", - "dft.bus_net_support.uk" + "dft.bus_net_support.uk", + "obr.fuel_duties_cars", + "orr.rail_government_support", + "ons.household_energy_expenditure", + "scotgov.bus.passenger_revenue", + "scotgov.bus.government_support", + "welshgov.bus.public_support", + "dfi_ni.bus.passenger_receipts", + "nithc.public_transport_support" ] }, "profile_parity": { @@ -12589,6 +12605,355 @@ "period_type": "fiscal_year" } }, + { + "target_id": "obr.fuel_duties_cars", + "family": "obr", + "geography_levels": [ + "country" + ], + "measurement": { + "entity": "household", + "concept": "uk.tax.fuel_duty.amount" + }, + "bindings": { + "policyengine": { + "metric_name": "obr/fuel_duties_cars", + "value_variable": "fuel_duty", + "notes": "OBR fuel duty receipts from cars (supplementary release by vehicle type), the household-incidence scope of the all-road-users obr.fuel_duties fact that stays signed out; the engine's fuel_duty is cars only (#890 E1, ruling 2026-09-10)." + }, + "axiom": { + "metric_name": "obr/fuel_duties_cars", + "status": "pending", + "value_rule": "uk.tax.fuel_duty.amount" + } + }, + "ledger_selector": { + "source_name": "obr", + "source_concept": "obr.fuel_duty.receipts", + "entity_name": "government", + "period_type": "fiscal_year", + "dimension_values": { + "vehicle_category": "cars" + } + }, + "assertion_policy": "allow_source_projection" + }, + { + "target_id": "orr.rail_government_support", + "family": "orr_rail", + "geography_levels": [ + "country" + ], + "measurement": { + "entity": "household", + "concept": "uk.transport.rail_support.amount", + "observation_basis": "fiscal_year_flow", + "filters": [ + { + "concept": "uk.geography.country", + "operator": "!=", + "value": "NORTHERN_IRELAND" + } + ] + }, + "bindings": { + "policyengine": { + "metric_name": "orr/rail_government_support", + "value_variable": "rail_subsidy_spending", + "filters": [ + { + "variable": "region", + "operator": "!=", + "value": "NORTHERN_IRELAND" + } + ], + "notes": "ORR table 7270 total government support to the rail industry, Great Britain, so Northern Ireland households are excluded from the measure; latest published fiscal year held to the calibration year. The engine recomputes rail_subsidy_spending as rail_usage x gov.dft.rail.fare_index (#890 G; 7270 broad total versus 7271 operational is the open ruling)." + }, + "axiom": { + "metric_name": "orr/rail_government_support", + "status": "pending", + "value_rule": "uk.transport.rail_support.amount" + } + }, + "ledger_selector": { + "source_name": "orr", + "source_concept": "orr.rail.total_government_support", + "entity_name": "government", + "period_type": "fiscal_year" + } + }, + { + "target_id": "ons.household_energy_expenditure", + "family": "ons_national_accounts", + "geography_levels": [ + "country" + ], + "measurement": { + "entity": "household", + "concept": "uk.consumption.domestic_energy.amount" + }, + "bindings": { + "policyengine": { + "metric_name": "ons/household_energy_expenditure", + "value_variable": "domestic_energy_consumption", + "notes": "ONS Consumer Trends COICOP 04.5 electricity, gas and other fuels, current prices, calendar year; the publisher class includes other fuels (heating oil, solid fuel), which the frame's electricity plus gas column does not carry, so the bound total is a few percent wide of the modelled scope (#890 F1)." + }, + "axiom": { + "metric_name": "ons/household_energy_expenditure", + "status": "pending", + "value_rule": "uk.consumption.domestic_energy.amount" + } + }, + "ledger_selector": { + "source_name": "ons", + "source_concept": "ons.household_expenditure.electricity_gas_other_fuels", + "entity_name": "household", + "period_type": "calendar_year", + "dimension_values": { + "coicop": "04.5", + "frequency": "annual" + } + } + }, + { + "target_id": "scotgov.bus.passenger_revenue", + "family": "dft_local_bus", + "geography_levels": [ + "country" + ], + "measurement": { + "entity": "household", + "concept": "uk.transport.bus_fare.amount", + "observation_basis": "fiscal_year_flow", + "filters": [ + { + "concept": "uk.geography.country", + "equals": "SCOTLAND" + } + ] + }, + "bindings": { + "policyengine": { + "metric_name": "scotgov/bus_passenger_revenue", + "value_variable": "bus_fare_spending", + "filters": [ + { + "variable": "region", + "operator": "==", + "value": "SCOTLAND" + } + ], + "notes": "Scottish Transport Statistics table 2.8 operator passenger revenue (non-concessionary), fiscal year; the Scotland leg of the local bus fare partition (#890)." + }, + "axiom": { + "metric_name": "scotgov/bus_passenger_revenue", + "status": "pending", + "value_rule": "uk.transport.bus_fare.amount" + } + }, + "ledger_selector": { + "source_name": "scotgov", + "source_concept": "scotgov.bus.operator_revenue", + "layout_groupby_value_id": "passenger_revenue", + "entity_name": "institutional_sector", + "period_type": "fiscal_year" + } + }, + { + "target_id": "scotgov.bus.government_support", + "family": "dft_local_bus", + "geography_levels": [ + "country" + ], + "measurement": { + "entity": "household", + "concept": "uk.transport.bus_support.amount", + "observation_basis": "fiscal_year_flow", + "filters": [ + { + "concept": "uk.geography.country", + "equals": "SCOTLAND" + } + ] + }, + "bindings": { + "policyengine": { + "metric_name": "scotgov/bus_government_support", + "value_variable": "bus_subsidy_spending", + "filters": [ + { + "variable": "region", + "operator": "==", + "value": "SCOTLAND" + } + ], + "notes": "Scottish Transport Statistics table 2.9 all government support (local authority support, concessionary fares reimbursement, Network Support Grant), the same net-support concept as DfT BUS05bi; the Scotland leg of the bus support partition (#890)." + }, + "axiom": { + "metric_name": "scotgov/bus_government_support", + "status": "pending", + "value_rule": "uk.transport.bus_support.amount" + } + }, + "ledger_selector": { + "source_name": "scotgov", + "source_concept": "scotgov.bus.government_support", + "entity_name": "government", + "period_type": "fiscal_year", + "dimension_values": { + "funding_component": "all_government_support" + } + } + }, + { + "target_id": "welshgov.bus.public_support", + "family": "dft_local_bus", + "geography_levels": [ + "country" + ], + "measurement": { + "entity": "household", + "concept": "uk.transport.bus_support.amount", + "observation_basis": "fiscal_year_flow", + "filters": [ + { + "concept": "uk.geography.country", + "equals": "WALES" + } + ] + }, + "bindings": { + "policyengine": { + "metric_name": "welshgov/bus_public_support", + "value_variable": "bus_subsidy_spending", + "filters": [ + { + "variable": "region", + "operator": "==", + "value": "WALES" + } + ], + "notes": "StatsWales roads and transport revenue outturn, Wales total, gross expenditure on concessionary fares plus support to operators (the two RO2 bus lines): public support routed through local authorities only; Welsh Government grants paid directly to operators and Wales fare revenue are not published, so the Wales fare leg stays unbound (#890)." + }, + "axiom": { + "metric_name": "welshgov/bus_public_support", + "status": "pending", + "value_rule": "uk.transport.bus_support.amount" + } + }, + "ledger_selector": { + "source_name": "welshgov", + "source_concept": "welshgov.local_transport.gross_expenditure", + "entity_name": "government", + "period_type": "fiscal_year", + "dimension_values": { + "expenditure_component": "Gross expenditure", + "service_line": [ + "Concessionary fares", + "Support to operators" + ] + } + } + }, + { + "target_id": "dfi_ni.bus.passenger_receipts", + "family": "dft_local_bus", + "geography_levels": [ + "country" + ], + "measurement": { + "entity": "household", + "concept": "uk.transport.bus_fare.amount", + "observation_basis": "fiscal_year_flow", + "filters": [ + { + "concept": "uk.geography.country", + "equals": "NORTHERN_IRELAND" + } + ] + }, + "bindings": { + "policyengine": { + "metric_name": "dfi_ni/bus_passenger_receipts", + "value_variable": "bus_fare_spending", + "filters": [ + { + "variable": "region", + "operator": "==", + "value": "NORTHERN_IRELAND" + } + ], + "notes": "DfI Public Transport Statistics, Translink bus passenger receipts, Ulsterbus plus Metro and Glider, fiscal year; the Northern Ireland leg of the local bus fare partition (#890)." + }, + "axiom": { + "metric_name": "dfi_ni/bus_passenger_receipts", + "status": "pending", + "value_rule": "uk.transport.bus_fare.amount" + } + }, + "ledger_selector": { + "source_name": "dfi_ni", + "source_concept": "dfi_ni.translink_bus.passenger_receipts", + "entity_name": "institutional_sector", + "period_type": "fiscal_year", + "dimension_values": { + "service": [ + "ulsterbus", + "metro_glider" + ] + } + } + }, + { + "target_id": "nithc.public_transport_support", + "family": "dft_local_bus", + "geography_levels": [ + "country" + ], + "measurement": { + "entity": "household", + "concept": "uk.transport.public_transport_support.amount", + "observation_basis": "fiscal_year_flow", + "filters": [ + { + "concept": "uk.geography.country", + "equals": "NORTHERN_IRELAND" + } + ] + }, + "bindings": { + "policyengine": { + "metric_name": "nithc/public_transport_support", + "value_expression": "bus_subsidy_spending + rail_subsidy_spending", + "filters": [ + { + "variable": "region", + "operator": "==", + "value": "NORTHERN_IRELAND" + } + ], + "notes": "Northern Ireland Transport Holding Company accounts, note 23: Public Service Obligation compensation plus concessionary fare compensation paid by DfI, all public transport (Ulsterbus, Metro, Glider and NI Railways; the accounts carry no bus/rail split), so the measure is bus plus rail support for Northern Ireland households (#890; the joint cell versus a declared bus share is the open ruling)." + }, + "axiom": { + "metric_name": "nithc/public_transport_support", + "status": "pending", + "value_rule": "uk.transport.public_transport_support.amount" + } + }, + "ledger_selector": { + "source_name": "nithc", + "source_concept": "nithc.dfi_transaction_value", + "entity_name": "government", + "period_type": "fiscal_year", + "dimension_values": { + "mode_scope": "all_public_transport", + "funding_component": [ + "Public Service Obligation compensation", + "Concessionary fare compensation for a range of groups" + ] + } + } + }, { "target_id": "hmrc.self_employment_income.amount", "family": "hmrc", diff --git a/packages/microcosm-build/tests/test_uk_bus_targets.py b/packages/microcosm-build/tests/test_uk_bus_targets.py index 6534b8eee..741247bdf 100644 --- a/packages/microcosm-build/tests/test_uk_bus_targets.py +++ b/packages/microcosm-build/tests/test_uk_bus_targets.py @@ -70,6 +70,7 @@ def test_bus_targets_are_active_or_signed_excluded_as_declared() -> None: assert {exclusions[target_id]["reason_id"] for target_id in excluded} == { "derived_partition_member", "no_publisher_uk_total", + "covered_by_country_legs", } assert all( membership["targets"][target_id]["status"] == "signed_excluded" diff --git a/packages/microcosm-build/tests/test_uk_measure_simulation.py b/packages/microcosm-build/tests/test_uk_measure_simulation.py index 75b724c8f..0253a714c 100644 --- a/packages/microcosm-build/tests/test_uk_measure_simulation.py +++ b/packages/microcosm-build/tests/test_uk_measure_simulation.py @@ -576,7 +576,10 @@ def test_exclusion_applier_warns_within_week_of_expiry(): "dwp/uc_payment_dist/": 18, "obr.universal_credit_": 2, "ons.household_composition.": 3, - "obr.fuel_duties": 1, + # microcosm#890 E1 (2026-09-11): the all-road-users obr.fuel_duties row + # is signed out of the reference surface (target_reference_signed_ + # exclusions.json) and obr.fuel_duties_cars binds the cars receipts, so + # the #757 measure exclusion for it retires here rather than lapsing. # microcosm#762 A16 (2026-09-03): the rows the spine cannot reach by # reweighting — savings interest, housing benefit, the two plan-2 # borrower stocks and JSA claimants — windowed to one month. The two @@ -600,7 +603,7 @@ def test_exclusion_applier_warns_within_week_of_expiry(): def test_packaged_exclusions_load(): exclusions = load_uk_calibration_measure_exclusions() names = [entry["name"] for entry in exclusions] - assert len(names) == len(set(names)) == 51 + assert len(names) == len(set(names)) == 50 for marker, expected in _PACKAGED_EXCLUSION_CENSUS.items(): matched = [name for name in names if marker in name] @@ -622,8 +625,11 @@ def test_packaged_exclusions_load(): # The 2026-08-26 tranche carries the uk_target_fit disposition # adjudication and a uniform three-month window; the ONS composition # cells track the relationship-to-head successor issue. + # 38 since microcosm#890 retired the obr.fuel_duties entry (2026-09-11): + # the all-road-users row is signed out of the reference surface and the + # cars receipts bind instead. tranche = [e for e in exclusions if e["approved_on"] == "2026-08-26"] - assert len(tranche) == 39 + assert len(tranche) == 38 for entry in tranche: assert "5427936411" in entry["adjudication"], entry["name"] assert entry["expires_on"] == "2026-11-26", entry["name"] diff --git a/packages/microcosm-build/tests/test_uk_national_calibration.py b/packages/microcosm-build/tests/test_uk_national_calibration.py index ad4615440..15c944399 100644 --- a/packages/microcosm-build/tests/test_uk_national_calibration.py +++ b/packages/microcosm-build/tests/test_uk_national_calibration.py @@ -41,7 +41,7 @@ from microcosm.calibrate import TargetRegistry, TargetSpec from microcosm.frame import EntitySchema, Frame, WeightKind, Weights -ACTIVE_REFERENCE_COUNT = 415 +ACTIVE_REFERENCE_COUNT = 424 def _uc_reference(**overrides) -> LedgerTargetReference: diff --git a/packages/microcosm-build/tests/test_uk_population_targets.py b/packages/microcosm-build/tests/test_uk_population_targets.py index db15e6704..8338e611b 100644 --- a/packages/microcosm-build/tests/test_uk_population_targets.py +++ b/packages/microcosm-build/tests/test_uk_population_targets.py @@ -180,15 +180,15 @@ def test_uk_population_targets_shape_order_and_registry_accounting() -> None: "operation": "sum", "assertion_policy": "observed_only", } - assert len(resource["targets"]) == 236 + assert len(resource["targets"]) == 244 target_ids = [target["target_id"] for target in resource["targets"]] registry_scope = resource["registry_parity"]["scope_target_ids"] profile_scope = resource["profile_parity"]["scope_target_ids"] - assert len(registry_scope) == 202 + assert len(registry_scope) == 210 assert len(profile_scope) == 34 - assert target_ids[:202] == registry_scope - assert target_ids[202:] == profile_scope + assert target_ids[:210] == registry_scope + assert target_ids[210:] == profile_scope parity = resource["registry_parity"] assert parity["pinned_ref"] == "12a1e028afeef08d8b2d74ee03fd9de3a78b2dd3" @@ -202,7 +202,7 @@ def test_uk_population_targets_shape_order_and_registry_accounting() -> None: assert mapped_target_ids | set(unmapped_declarations) == set(registry_scope) assert all(reason for reason in unmapped_declarations.values()) assert len(mapped_target_ids) == 183 - assert len(unmapped_declarations) == 19 + assert len(unmapped_declarations) == 27 suppressed_ancestors = parity["suppressed_ancestors"] assert len(suppressed_ancestors) == 5 assert set(suppressed_ancestors).isdisjoint(parity["mapped"]) @@ -321,7 +321,7 @@ def test_uk_population_targets_have_unique_target_ids() -> None: resource = _load() target_ids = [target["target_id"] for target in resource["targets"]] - assert len(target_ids) == 236 + assert len(target_ids) == 244 assert len(target_ids) == len(set(target_ids)) @@ -757,7 +757,7 @@ def test_uc_payment_bands_share_administrative_family_but_keep_source_window() - def test_paid_joint_diagnostics_do_not_add_active_targets() -> None: targets = _load()["targets"] - assert len(targets) == 236 + assert len(targets) == 244 assert not any( f.get("variable") == "uc_calibration_child_entitlement" for target in targets diff --git a/packages/microcosm-build/tests/test_uk_target_references.py b/packages/microcosm-build/tests/test_uk_target_references.py index 940a30af0..4656704dd 100644 --- a/packages/microcosm-build/tests/test_uk_target_references.py +++ b/packages/microcosm-build/tests/test_uk_target_references.py @@ -56,7 +56,7 @@ _value_operation_by_target_id, ) -ACTIVE_REFERENCE_COUNT = 417 +ACTIVE_REFERENCE_COUNT = 424 UK_DATA_REPO = "policyengine-" + "uk-data" @@ -300,7 +300,15 @@ def test_childcare_and_bus_references_compile_with_declared_provenance() -> None source_name = fact.get("source", {}).get("source_name") or fact.get( "observed_measure", {} ).get("source_name") - if source_name in {"hmrc", "dfe", "dft"}: + if source_name in { + "hmrc", + "dfe", + "dft", + "scotgov", + "welshgov", + "dfi_ni", + "nithc", + }: facts.append(fact) registry = compile_ledger_target_references(facts, references, country="uk") @@ -315,6 +323,11 @@ def test_childcare_and_bus_references_compile_with_declared_provenance() -> None "dft.bus_net_support.england": 3_024_904_320.8399997, "dft.bus_fare_receipts.london": 1_347_434_943.01459, "dft.bus_net_support.london": 1_130_214_000.0, + "scotgov.bus.passenger_revenue": 391_000_000.0, + "scotgov.bus.government_support": 499_000_000.0, + "welshgov.bus.public_support": 131_489_970.0, + "dfi_ni.bus.passenger_receipts": 150_082_817.48999995, + "nithc.public_transport_support": 111_700_000.0, } assert { name: spec.metadata["ledger_entity_name"] for name, spec in specs.items() @@ -328,6 +341,11 @@ def test_childcare_and_bus_references_compile_with_declared_provenance() -> None "dft.bus_net_support.england": "institutional_sector", "dft.bus_fare_receipts.london": "institutional_sector", "dft.bus_net_support.london": "institutional_sector", + "scotgov.bus.passenger_revenue": "institutional_sector", + "scotgov.bus.government_support": "government", + "welshgov.bus.public_support": "government", + "dfi_ni.bus.passenger_receipts": "institutional_sector", + "nithc.public_transport_support": "government", } assert { name: ( @@ -342,6 +360,20 @@ def test_childcare_and_bus_references_compile_with_declared_provenance() -> None "dft.bus_fare_receipts.london": ("region", "E12000007"), "dft.bus_net_support.london": ("region", "E12000007"), } + assert { + name: ( + spec.metadata["ledger_geography_level"], + spec.metadata["ledger_geography_id"], + ) + for name, spec in specs.items() + if name.split(".", 1)[0] in {"scotgov", "welshgov", "dfi_ni", "nithc"} + } == { + "scotgov.bus.passenger_revenue": ("country", "S92000003"), + "scotgov.bus.government_support": ("country", "S92000003"), + "welshgov.bus.public_support": ("country", "W92000004"), + "dfi_ni.bus.passenger_receipts": ("country", "N92000002"), + "nithc.public_transport_support": ("country", "N92000002"), + } assert ( specs["dfe.funded_childcare.universal_only_children"].metadata[ "ledger_value_formula" @@ -492,6 +524,8 @@ def test_prefix_geography_pins_carry_scotgov_and_england_scoped_slc_families() - } | { "scotgov.council_tax_stock.total", "scotgov.scottish_child_payment_spending", + "scotgov.bus.passenger_revenue", + "scotgov.bus.government_support", } assert {pins[target_id]["geography_id"] for target_id in scotgov_ids} == { "S92000003" @@ -561,9 +595,9 @@ def test_uk_target_reference_membership_report_is_packaged() -> None: assert membership["target_period"] == 2025 assert membership["active_reference_count"] == ACTIVE_REFERENCE_COUNT assert membership["status_counts"] == { - "active": 417, + "active": 424, "no_fact_at_or_before_period": 7, - "signed_excluded": 6, + "signed_excluded": 7, } assert membership["genuine_sum_residue"] assert membership["uprating_holds"] From a256c88009b7b59b5ebe5f480bc47c62c9a25a65 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mar=C3=ADa=20Juaristi?= <127882282+juaristi22@users.noreply.github.com> Date: Fri, 11 Sep 2026 13:46:39 +0200 Subject: [PATCH 06/10] Defer the cars fuel-duty fence to PR-S and record the PR-T baseline receipts (#890) On the newest gated national spine (spine-p) with the PR-T surface, every new row fits inside the 25% bound after calibration except obr.fuel_duties_cars at -25.6% (from -46.6% before the solve). That row gets a dated reviewed target-fit exclusion (approved 2026-09-11, expires 2026-10-11, naming the baseline run) because both of its fixes are the spine half: the LCFS car-count predictor and the donor litre-proxy uprating. The row stays bound; only the release fence defers. A second run with the entry in force passes the battery. The receipts record what the fit costs today: the London bus rows, the Scotland and Northern Ireland legs and the ORR rail row are closed by weight stretch on the households that carry the imputed columns (73-85% of London bus mass and 71% of rail support mass on households stretched more than 5x), which is the distortion the value-side stage work in PR-S removes; ONS household energy expenditure already sits on the frame at design weights. The ORR 7271 all-sources figure is recorded for the open 7270-versus-7271 rail ruling. Co-Authored-By: Claude Fable 5.1 --- changelog.d/uk-890-target-binding.added.md | 1 + experiments/890-fuel-bus-energy-receipts.md | 131 ++++++++++++++++++ .../uk/target_fit_reviewed_exclusions.json | 10 +- .../tests/test_country_spec.py | 2 +- .../tests/test_uk_terminal_gates.py | 23 ++- 5 files changed, 160 insertions(+), 7 deletions(-) create mode 100644 changelog.d/uk-890-target-binding.added.md create mode 100644 experiments/890-fuel-bus-energy-receipts.md diff --git a/changelog.d/uk-890-target-binding.added.md b/changelog.d/uk-890-target-binding.added.md new file mode 100644 index 000000000..caab664c9 --- /dev/null +++ b/changelog.d/uk-890-target-binding.added.md @@ -0,0 +1 @@ +Bind the transport and energy facts of chronicle #254/#257 on the UK national surface (#890, the target-binding half of the #790 "bind facts and calibrate" ruling). The DfT London bus rows bind at region level through a region-aware generator pin and a compile-time roster check; England outside London is derived from the bound England and London rows; BUS05ai fare receipts are aligned from the year ending March to the calibration calendar year with the BUS0415 fares index (England ×1.057, London ×1.0; net support never aligned), with the factor and its source record ids declared on the spec metadata. Eight net-new national targets bind pinned facts: OBR fuel duty receipts from cars on the engine's cars-only `fuel_duty` (the all-road-users `obr.fuel_duties` row is signed out and its #757 measure exclusion retires), ORR table 7270 rail support (Great Britain) on `rail_subsidy_spending`, ONS Consumer Trends COICOP 04.5 on `domestic_energy_consumption`, and the Scotland, Wales and Northern Ireland local-bus legs (Scottish Transport Statistics passenger revenue and government support; StatsWales outturn support through local authorities; DfI Translink receipts; NITHC public transport support as a joint bus-plus-rail cell). Contract 236 → 244 targets, active references 415 → 424, national compile 424/0 unsupported. One dated reviewed target-fit exclusion (`obr.fuel_duties_cars@2025`, −25.6% on the spine-p baseline, expires 2026-10-11) defers the release fence until the spine half lands; the baseline receipts in `experiments/890-fuel-bus-energy-receipts.md` record that every other new row fits by weight stretch alone, which is what the spine half is built to remove. diff --git a/experiments/890-fuel-bus-energy-receipts.md b/experiments/890-fuel-bus-energy-receipts.md new file mode 100644 index 000000000..081e1642f --- /dev/null +++ b/experiments/890-fuel-bus-energy-receipts.md @@ -0,0 +1,131 @@ +# #890 receipts — bind DfT, HMRC, DESNZ, Ofgem, ORR and NTS facts for bus fares, road fuel, energy and rail + +Plan: `repos/uk-890-implementation-plan.md` (María's rulings 2026-09-10/11: OBR cars receipts as +the fuel target; BUS0415 alignment on a calendar-year basis; donors uprated to FY2024-25; bus +incidence modelled take-up style on NTS frequency of use; two PRs, target binding first). Licensed +evidence lives under `data/ukds/acceptance/890-fuel-bus/` (SDC-safe aggregates only); this file +carries the digest-pinned receipts the PR's declarations anchor to. + +## PR-T — calibration target binding + +### Part A — Chronicle feed re-pin (national surface only) + +Feed rebuilt from `PolicyEngine/chronicle` main `c6f9361492056b9fab7b8535a2be77eb2b6c93bb` +(2026-09-11, the merge of #258 on top of #255) with `chronicle build-bundle --suite uk` → +`build-consumer-artifact` (built 2026-09-11 from a worktree at that commit): + +- `consumer_facts.jsonl` sha256 `45bda3ae730d4ae3fa059d9e03304e902f7f6e74c5099355ef937625ca03b72b` +- `manifest.json` sha256 `33a3031523e2cea2f0092a547b97065efbe103cdf85f842142e847b921bcdd9d` +- rows / schema: 138,847 / `policyengine_ledger.consumer_artifact.v2`; consumer-fact schema + digest `72ad3149…` (was `76ac268e…`: chronicle added the `quarter` and `week` period types) +- previous national pin: `ec7169b`, 131,450 rows (`4a50ee95…` / `a95d0ee9…`) + +Effect on the committed national surface before any binding change: none. 415 active, 7 +deferred, 8 signed out, no resolved value or fact key moved; the compile-parity receipts +regenerate byte-identical. Only the membership feed label moves +(`chronicle-uk-ec7169b5-consumer-facts` → `chronicle-uk-c6f93614-consumer-facts`). The local +census, validation levels and local reference surfaces keep their separately reviewed +`ec7169b` pin; the local regeneration test now reads its own default file +(`.codex-work/consumer_facts_uk_local.jsonl`). + +### Part B — vendored Chronicle facts (`tools/vendor_uk_ledger_facts.py`) + +Nine per-concern resources under `uk/`, each carrying the feed identity above and +regenerating byte-identical from it under test; selection counts are asserted by the register +`uk/ledger_fact_vendor_selections.json`: + +- `dft_bus_value_anchors.json` — 81 rows (BUS05ai 9, BUS05bi 9, BUS0415 39, NTS0705a 24); sha256 `2a92a656…` +- `road_fuel_anchors.json` — 69 rows (HMRC 12, DESNZ annual 6, OBR by vehicle 49, ONS population 2); `8dc10402…` +- `licensed_cars_fuel_type.json` — 36 rows; `83750dff…` +- `need_energy_facts.json` — 228 rows (E&W 144, Scotland 84); `5453d7fd…` +- `ofgem_price_cap_facts.json` — 268 rows (cap levels 256, benchmark consumption 12); `b668e351…` +- `nts_bus_use_frequency.json` — 54 rows (NTS0313 27, NTS0621 27); `2738c4f5…` +- `devolved_bus_finance.json` — 86 rows (Scotland 18, Wales 36, NITHC 8, DfI 24); `6bd24101…` +- `orr_rail_facts.json` — 99 rows; `02c8870b…` +- `ons_household_expenditure_facts.json` — 18 rows; `7c13e88a…` + +### Part C — region leg and the London bus rows + +- `_geography_pins` pins a region-only contract target at region level; the compiler refuses a + region pin outside the nine English region codes (`UK_NATIONAL_REGION_ROSTER`) and a + region-pinned reference that resolves a non-region fact. +- `dft.bus_fare_receipts.london` → fact FY label 2025 (comparable 2024), GBP 1,347,434,943.01; + `dft.bus_net_support.london` → GBP 1,130,214,000; both region `E12000007`. +- England outside London: signed exclusion `derived_partition_member` (the partition + reconciles within GBP 1,000 in `test_uk_bus_targets.py`). UK fares row: `no_publisher_uk_total` + (Wales fare revenue unpublished). UK support row: `covered_by_country_legs`. + +### Part D — BUS0415 alignment (fares only) + +Factor = mean of the four quarter-end index values inside the calibration calendar year over +the mean of the four inside the receipts' April-to-March fiscal year, per series: + +- England `E92000001`: from (2024-06, 2024-09, 2024-12, 2025-03) mean 193.125; to (2025-03, + 2025-06, 2025-09, 2025-12) mean 204.125; factor 1.05695792880259; value GBP 3,417,388,656.44 + → 3,612,036,036.22. +- London `E12000007`: 196.1 flat in both windows; factor 1.0. +- Net support rows are never aligned (#789 scope item 5). + +### Part E — new national targets (pinned facts, registry scope, unmapped declarations) + +- `obr.fuel_duties_cars`: OBR receipts by vehicle category, cars, FY2025 (projection, obr + family policy) GBP 15,900,000,000 on `fuel_duty`. `obr.fuel_duties` (all road users) signed + out; its #757 measure exclusion retired (register 51 → 50 entries). +- `orr.rail_government_support`: ORR 7270, FY2024 GBP 21,621,100,549 held to 2025, Great + Britain (`region != NORTHERN_IRELAND`), on `rail_subsidy_spending`. +- `ons.household_energy_expenditure`: ONS COICOP 04.5 CY2025 GBP 43,427,000,000 on + `domestic_energy_consumption`. +- `scotgov.bus.passenger_revenue` GBP 391,000,000; `scotgov.bus.government_support` + GBP 499,000,000 (FY2024, region SCOTLAND). +- `welshgov.bus.public_support` GBP 131,489,970 (FY2024, concessionary fares GBP 62,443,460 + + support to operators GBP 69,046,510 gross, region WALES). +- `dfi_ni.bus.passenger_receipts` GBP 150,082,817.49 (FY2024, Ulsterbus + Metro/Glider, + region NORTHERN_IRELAND). +- `nithc.public_transport_support` GBP 111,700,000 (FY2024, PSO 61.8m + concessionary 49.9m, + bus plus rail) on `bus_subsidy_spending + rail_subsidy_spending`, region NORTHERN_IRELAND. + +Surface after Part E: 244 contract targets (registry scope 210, profile scope 34), 424 active +references, 7 deferred, 7 signed out; national runtime compile 424 / 0 unsupported. + +### Part F — baseline national calibration on the current spine + +(filled from `data/ukds/acceptance/890-fuel-bus/pr-t-baseline-spine-p/` once the run completes) + +Runs on the newest gated national spine (`spine-p`, `data/ukds/acceptance/spine-p-355/spine-p.h5`, +sha `ae83e307…`, FRS 2024-25, stamped 2024), 1,500 epochs, `family_equal`, code `3a135c5f`: + +- `pr-t-baseline-spine-p/` (`uk-frs-calibration-attempt-20260911T113749Z-0c7e79e2`): the terminal + battery blocked on one row, `obr.fuel_duties_cars@2025` at −25.6%; every other new row inside the + bound. Loss 0.01154, 96.0% within 10%, ESS 10,461 (0.198), max/median 100, 374 targets solved + (424 compiled − 50 measure exclusions). +- `pr-t-baseline-spine-p-2/` (`…20260911T114304Z-61b7818a`): identical solve with the dated + reviewed exclusion for that row in force (register entry approved 2026-09-11, expires + 2026-10-11, tracking the PR-S landing); battery passes, staging H5 written (not a release + candidate). + +Pre- and post-calibration fit of the new rows (initial → final relative error): + +- bus fares England −46.3% → −0.0%; London −72.4% → +0.1%; support England −28.8% → +0.1%, + London −72.5% → +0.1% +- `obr.fuel_duties_cars` −46.6% → −25.6% (excluded from the fence, still bound) +- `orr.rail_government_support` −71.8% → +0.2% +- `ons.household_energy_expenditure` +0.2% → +0.0% +- Scotland fares −63.0% → +0.0%; Scotland support −54.5% → +0.1%; Wales support −9.9% → +0.2%; + NI fares −65.3% → −0.0%; NI public transport support +65.3% → −0.2% + +How the solver closed them, measured on the calibrated staging H5 against spine-p design +weights (the receipt PR-S is built to change): the closures are weight stretch on the households +carrying the imputed column, not level. Share of the calibrated mass sitting on households whose +weight rose more than 3× (more than 5× in brackets): London fares 83% (73%), London support 85% +(77%), England fares 60% (48%), Scotland fares 64% (33%), Scotland support 69% (57%), NI fares +78% (3%), rail support GB 83% (71%), motor fuel 51%. Rail is the largest single stretch: the +frame carries GBP 6.1bn of `rail_subsidy_spending` at design weights against the ORR 7270 broad +total of GBP 21.6bn, so the 14% of households with rail use are stretched ×3.6 on average; the +7271 all-sources operational figure for FY2024 is recorded below for the open 7270-versus-7271 +ruling. Northern Ireland public transport support runs the other way (frame GBP 185m at design +weights against GBP 112m published), because the ETB donor is a GB survey. Domestic energy +already sits on ONS 04.5 at design weights (GBP 43.5bn against 43.4bn), so that row costs +nothing; PR-S repricing to FY2024-25 cap rates with standing charges will move the frame off it +and is the point at which the NEED-versus-ONS level question is decided. + +ORR FY2024 for the rail ruling: table 7270 total government support GBP 21.62bn (bound); table 7271 all sources GBP 11.86bn, of which Department for Transport GBP 9.47bn. diff --git a/packages/microcosm-build/src/microcosm/build/uk/target_fit_reviewed_exclusions.json b/packages/microcosm-build/src/microcosm/build/uk/target_fit_reviewed_exclusions.json index 1e9d92c1d..44b787184 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/target_fit_reviewed_exclusions.json +++ b/packages/microcosm-build/src/microcosm/build/uk/target_fit_reviewed_exclusions.json @@ -1,5 +1,13 @@ { "schema_version": 2, "description": "Reviewed target-fit exclusions for the UK terminal battery (schema 2 per #610). Each entry defers one calibration target whose |relative error| breaches the 25% release bound, with the defect diagnosed, tracked, and owned elsewhere; the receipt records the reasoning, approver, adjudication reference, approval date, and expiry. The gate keeps the target BOUND in calibration (exclusion applies to the release fence only, never the solve), reports dormant entries, FAILS stale ones (targets back inside the bound), and FAILS expired ones until the adjudication is renewed, so this register cannot rot.", - "exclusions": {} + "exclusions": { + "obr.fuel_duties_cars@2025": { + "reason": "Household-incidence fuel duty binds for the first time (OBR cars receipts, FY2025 GBP 15.9bn). On the current spine the frame reaches -25.6% after calibration from -46.6% before: the LCFS-imputed petrol and diesel spend loses about a fifth of the donor's mass to the uninformative WAS has-fuel flag and its recipient zeroing, and the donor is not yet uprated to FY2024-25. Both fixes are the spine half of #890 (PR-S: LCFS car count as the predictor, donor litre-proxy uprating). The row stays bound; this defers the release fence only until PR-S lands.", + "approved_by": "juaristi22", + "adjudication": "microcosm#890 plan of record (repos/uk-890-implementation-plan.md, approved 2026-09-10): dated reviewed exclusions for rows that cannot fit until the value side lands in PR-S; measured on the PR-T baseline run uk-frs-calibration-attempt-20260911T113749Z-0c7e79e2 (spine-p)", + "approved_on": "2026-09-11", + "expires_on": "2026-10-11" + } + } } diff --git a/packages/microcosm-build/tests/test_country_spec.py b/packages/microcosm-build/tests/test_country_spec.py index f33b2a71e..975d51fa2 100644 --- a/packages/microcosm-build/tests/test_country_spec.py +++ b/packages/microcosm-build/tests/test_country_spec.py @@ -1081,7 +1081,7 @@ def test_uk_target_references_accept_regenerated_contract_fields(self) -> None: spec = load_country_spec("uk") references = {reference.name: reference for reference in spec.target_references} - assert len(references) == 415 + assert len(references) == 424 assert references["obr.esa"].value_operation == "sum" assert references["dwp.uc.households"].value_operation == ( "monthly_window_sum_average" diff --git a/packages/microcosm-build/tests/test_uk_terminal_gates.py b/packages/microcosm-build/tests/test_uk_terminal_gates.py index b1fa9cdb6..c42e6d572 100644 --- a/packages/microcosm-build/tests/test_uk_terminal_gates.py +++ b/packages/microcosm-build/tests/test_uk_terminal_gates.py @@ -389,7 +389,15 @@ def test_target_fit_out_of_force_exclusion_fails_even_without_a_breach() -> None def test_committed_target_fit_register_retains_only_live_deferrals() -> None: register = uk_default_target_fit_reviewed_exclusions() - assert register == {} + # microcosm#890 PR-T: the household-incidence fuel duty row binds before the + # spine half lands; the entry is dated to the PR-S window and names the + # baseline run it was measured on. + assert set(register) == {"obr.fuel_duties_cars@2025"} + entry = register["obr.fuel_duties_cars@2025"] + assert str(entry.approved_on) == "2026-09-11" + assert str(entry.expires_on) == "2026-10-11" + assert "PR-S" in entry.reason + assert "20260911T113749Z-0c7e79e2" in entry.adjudication # Aggregate errors from the fresh UC #882 development run: 1,500 epochs with @@ -414,13 +422,13 @@ def test_restored_fit_checks_leave_empty_payment_tail_cells_blocked() -> None: **empty_tail, }, reviewed_exclusions=uk_default_target_fit_reviewed_exclusions(), - now=date(2026, 9, 9), + now=date(2026, 9, 15), ) assert not fit.passed assert fit.details["stale_exclusions"] == [] assert fit.details["failing_targets"] == empty_tail - assert fit.details["reviewed_exclusions"] == {} + assert set(fit.details["reviewed_exclusions"]) <= {"obr.fuel_duties_cars@2025"} @pytest.mark.parametrize("name", sorted(_RESTORED_TARGET_FIT_ERRORS)) @@ -436,11 +444,16 @@ def test_restored_fit_checks_apply_if_a_later_run_breaches_again( fit = uk_target_fit_gate( {name: relative_error}, reviewed_exclusions=uk_default_target_fit_reviewed_exclusions(), - now=date(2026, 9, 9), + now=date(2026, 9, 15), ) assert fit.passed is passes - assert fit.details["reviewed_exclusions"] == {} + # The one live entry (obr.fuel_duties_cars, #890) never covers these rows: + # it is recorded as reviewed or dormant, and the breach stays a failure. + recorded = set(fit.details["reviewed_exclusions"]) | set( + fit.details.get("dormant_exclusions", ()) + ) + assert recorded <= {"obr.fuel_duties_cars@2025"} assert fit.details["failing_targets"] == ({} if passes else {name: relative_error}) From 470bc0d50115f2e02be9459ac4274769aa39255c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mar=C3=ADa=20Juaristi?= <127882282+juaristi22@users.noreply.github.com> Date: Fri, 11 Sep 2026 16:45:03 +0200 Subject: [PATCH 07/10] Fan out the two-level UK targets over the region tier (#905) The 18 country+region contract targets (ONS population by age band, VOA council-tax stock by band) compiled to one country row each because the national generator pins every target to a single geography. They now fan out over the twelve-area region tier declared in geography_ladder (UK_REGION_TIER: the nine English regions at Chronicle's region level, Wales, Scotland and Northern Ireland at country level), one reference per cell named and measured target_id@geography_id, through a new shared authoring option (geography_fanout_by_target_id + geography_fanout_metadata on TargetReferenceAuthoringConfig). The VOA family fans over the English regions only, because its binding already pins country == ENGLAND. An unsigned absent cell refuses the run, as the area-grain authoring does. The 18 single country rows are retired: the twelve ONS cells of a band sum to the former UK row within the same publication (0-9: 7,553,013 at mid-2024). Register: 595 active (424 - 18 + 189); the 406 untouched references regenerate byte-identically; membership records the fan-out roster per target under geography_pins. Contract binding notes describe our mechanism instead of the incumbent's compute_regional_age. Co-Authored-By: Claude Fable 5.1 --- .../905-uk-region-grain-activation.added.md | 1 + .../build/target_reference_authoring.py | 113 +- .../build/uk/target_reference_membership.json | 9566 ++++++++++++----- .../microcosm/build/uk/target_references.json | 7897 ++++++++++++-- .../build/uk/uk_population_targets.json | 36 +- .../microcosm/build/uk_runtime/__init__.py | 6 + .../build/uk_runtime/geography_ladder.py | 66 +- .../tests/test_country_spec.py | 2 +- .../tests/test_target_reference_authoring.py | 158 + .../tests/test_uk_geography_ladder.py | 36 +- .../tests/test_uk_national_calibration.py | 2 +- .../tests/test_uk_target_references.py | 137 +- tools/generate_uk_target_references.py | 124 +- 13 files changed, 14255 insertions(+), 3889 deletions(-) create mode 100644 changelog.d/905-uk-region-grain-activation.added.md diff --git a/changelog.d/905-uk-region-grain-activation.added.md b/changelog.d/905-uk-region-grain-activation.added.md new file mode 100644 index 000000000..67160e299 --- /dev/null +++ b/changelog.d/905-uk-region-grain-activation.added.md @@ -0,0 +1 @@ +Activate UK region grain: the 18 two-level (country + region) contract targets — ONS population by age band and VOA council-tax stock by band — fan out over the twelve-area region tier (nine English regions, Wales, Scotland, Northern Ireland), one reference per area scoped on the spine by a household-region predicate, replacing the 18 single country rows; the cross-grain rule gains `region` between country and constituency with every tier code as its own leg, resolved for English areas through the crosswalk's ladder-derived region membership (microcosm#905). diff --git a/packages/microcosm-build/src/microcosm/build/target_reference_authoring.py b/packages/microcosm-build/src/microcosm/build/target_reference_authoring.py index 477af0e3f..2ff386f23 100644 --- a/packages/microcosm-build/src/microcosm/build/target_reference_authoring.py +++ b/packages/microcosm-build/src/microcosm/build/target_reference_authoring.py @@ -27,6 +27,15 @@ FanoutName = Callable[[Mapping[str, Any], Mapping[str, Any]], str | None] GeographyPin = Mapping[str, str] +#: ``(target, geography_level, geography_id, entity) -> extra reference metadata`` +#: for one geography fan-out row. Values are strings (reference metadata is a +#: string mapping); a country encodes structured values, such as a predicate, +#: as JSON. +GeographyFanoutMetadata = Callable[ + [Mapping[str, Any], str, str, str], Mapping[str, str] +] +#: Ordered ``(geography_level, geography_id)`` pairs one target fans out over. +GeographyFanout = tuple[tuple[str, str], ...] @dataclass(frozen=True) @@ -59,6 +68,18 @@ class TargetReferenceAuthoringConfig: default_factory=dict ) signed_exclusions_by_target_id: Mapping[str, str] = field(default_factory=dict) + #: Targets that fan out over a declared geography roster instead of pinning + #: one geography: one reference per ``(geography_level, geography_id)``, + #: named ``target_id@geography_id`` with its own measure column, the same + #: way area-grain references are authored. A target listed here must not + #: also carry a geography pin. Every roster cell must compile; an absent + #: fact refuses the run rather than deferring silently. + geography_fanout_by_target_id: Mapping[str, GeographyFanout] = field( + default_factory=dict + ) + #: Optional per-row metadata for geography fan-out rows (for example the + #: predicate that scopes the measure to the row's geography). + geography_fanout_metadata: GeographyFanoutMetadata | None = None binding_vocabulary: frozenset[str] = frozenset() source_fact_feed: str = "" @@ -147,7 +168,22 @@ def author_target_references( for target in contract.get("targets", ()): target_id = str(target["target_id"]) pin = config.geography_pins.get(target_id, {}) - geography_pin_report[target_id] = dict(pin) + geography_fanout = config.geography_fanout_by_target_id.get(target_id) + if geography_fanout: + if pin: + raise ValueError( + f"target {target_id!r} declares both a geography pin " + f"{dict(pin)!r} and a geography fan-out; a fanned-out " + "target takes its geography from the roster only." + ) + geography_pin_report[target_id] = { + "geography_fanout": [ + {"geography_level": str(level), "geography_id": str(area_id)} + for level, area_id in geography_fanout + ] + } + else: + geography_pin_report[target_id] = dict(pin) source_facts = _source_prefilter(fact_rows, facts_by_source, target) selector = _target_selector(target, pin, config) signed_exclusion = config.signed_exclusions_by_target_id.get(target_id) @@ -192,6 +228,10 @@ def author_target_references( "matched_fact_count_overall": len(matched), "matched_fact_count_at_or_before_period": len(eligible), } + row_geography_level = str(row["metadata"].get("geography_level") or "") + if row_geography_level: + entry["geography_level"] = row_geography_level + entry["geography_id"] = str(row["metadata"].get("geography_id") or "") if reference.period_match_policy == "source_window": entry["matched_fact_count_in_source_window"] = sum( _assertion_allowed(reference, fact) for fact in matched @@ -205,6 +245,15 @@ def author_target_references( except ValueError as error: entry["status"] = _classify_deferral(error, matched, eligible) entry["error"] = _compact_compile_error(entry["status"]) + if geography_fanout: + # A roster cell that does not compile is a hole in a + # declared partition, not a deferral: refuse, the way the + # area-grain authoring refuses an unsigned absence. + raise ValueError( + f"Unsigned geography fan-out absence for {target_id!r} " + f"at {entry.get('geography_level')!r}/" + f"{entry.get('geography_id')!r}: {entry['status']}." + ) from error else: spec = registry.specs[0] resolved_period = spec.metadata.get("ledger_fact_period", "") @@ -501,6 +550,12 @@ def _candidate_rows( geography_pin: GeographyPin, config: TargetReferenceAuthoringConfig, ) -> Iterable[dict[str, Any]]: + geography_fanout = config.geography_fanout_by_target_id.get( + str(target["target_id"]) + ) + if geography_fanout: + yield from _geography_fanout_rows(target, geography_fanout, config) + return selector = _target_selector(target, geography_pin, config) if "groupby_dimension" in selector: rows = _fanout_rows(target, facts, selector, config) @@ -510,6 +565,62 @@ def _candidate_rows( yield _reference_row(target, selector, config) +def _geography_fanout_rows( + target: Mapping[str, Any], + areas: GeographyFanout, + config: TargetReferenceAuthoringConfig, +) -> Iterable[dict[str, Any]]: + """One reference per roster cell, named and measured ``target_id@area``. + + The selector is the contract selector plus the cell's geography, exactly + as a pinned reference is built, so every other declaration (value + operation, member count, period policy, uprating holds) follows the + contract unchanged. The row's metadata carries the cell's geography under + the same keys area-grain references use, plus whatever the country's + ``geography_fanout_metadata`` adds for the cell. + """ + + target_id = str(target["target_id"]) + seen: set[str] = set() + for geography_level, geography_id in areas: + level = str(geography_level) + area_id = str(geography_id) + if not level or not area_id: + raise ValueError( + f"target {target_id!r} geography fan-out has a blank cell " + f"({geography_level!r}, {geography_id!r})." + ) + if area_id in seen: + raise ValueError( + f"target {target_id!r} geography fan-out lists {area_id!r} twice." + ) + seen.add(area_id) + selector = _target_selector( + target, + {"geography_level": level, "geography_id": area_id}, + config, + ) + row = _reference_row(target, selector, config, name=f"{target_id}@{area_id}") + metadata: dict[str, str] = { + **row["metadata"], + "geography_level": level, + "geography_id": area_id, + } + if config.geography_fanout_metadata is not None: + extra = config.geography_fanout_metadata( + target, level, area_id, str(row["entity"]) + ) + for key, value in extra.items(): + if not isinstance(value, str): + raise ValueError( + f"target {target_id!r} geography fan-out metadata " + f"{key!r} must be a string, got {type(value).__name__}." + ) + metadata[str(key)] = value + row["metadata"] = metadata + yield row + + def _target_selector( target: Mapping[str, Any], geography_pin: GeographyPin, diff --git a/packages/microcosm-build/src/microcosm/build/uk/target_reference_membership.json b/packages/microcosm-build/src/microcosm/build/uk/target_reference_membership.json index 771dbabbe..ba529e60c 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/target_reference_membership.json +++ b/packages/microcosm-build/src/microcosm/build/uk/target_reference_membership.json @@ -1,11 +1,11 @@ { "source_fact_feed": "chronicle-uk-c6f93614-consumer-facts", "target_period": 2025, - "candidate_count": 438, + "candidate_count": 609, "contract_target_count": 210, - "active_reference_count": 424, + "active_reference_count": 595, "status_counts": { - "active": 424, + "active": 595, "no_fact_at_or_before_period": 7, "signed_excluded": 7 }, @@ -363,40 +363,472 @@ "geography_id": "K02000001" }, "ons.population.age_0_9_by_region": { - "geography_level": "country", - "geography_id": "K02000001" + "geography_fanout": [ + { + "geography_level": "region", + "geography_id": "E12000001" + }, + { + "geography_level": "region", + "geography_id": "E12000002" + }, + { + "geography_level": "region", + "geography_id": "E12000003" + }, + { + "geography_level": "region", + "geography_id": "E12000004" + }, + { + "geography_level": "region", + "geography_id": "E12000005" + }, + { + "geography_level": "region", + "geography_id": "E12000006" + }, + { + "geography_level": "region", + "geography_id": "E12000007" + }, + { + "geography_level": "region", + "geography_id": "E12000008" + }, + { + "geography_level": "region", + "geography_id": "E12000009" + }, + { + "geography_level": "country", + "geography_id": "W92000004" + }, + { + "geography_level": "country", + "geography_id": "S92000003" + }, + { + "geography_level": "country", + "geography_id": "N92000002" + } + ] }, "ons.population.age_10_19_by_region": { - "geography_level": "country", - "geography_id": "K02000001" + "geography_fanout": [ + { + "geography_level": "region", + "geography_id": "E12000001" + }, + { + "geography_level": "region", + "geography_id": "E12000002" + }, + { + "geography_level": "region", + "geography_id": "E12000003" + }, + { + "geography_level": "region", + "geography_id": "E12000004" + }, + { + "geography_level": "region", + "geography_id": "E12000005" + }, + { + "geography_level": "region", + "geography_id": "E12000006" + }, + { + "geography_level": "region", + "geography_id": "E12000007" + }, + { + "geography_level": "region", + "geography_id": "E12000008" + }, + { + "geography_level": "region", + "geography_id": "E12000009" + }, + { + "geography_level": "country", + "geography_id": "W92000004" + }, + { + "geography_level": "country", + "geography_id": "S92000003" + }, + { + "geography_level": "country", + "geography_id": "N92000002" + } + ] }, "ons.population.age_20_29_by_region": { - "geography_level": "country", - "geography_id": "K02000001" + "geography_fanout": [ + { + "geography_level": "region", + "geography_id": "E12000001" + }, + { + "geography_level": "region", + "geography_id": "E12000002" + }, + { + "geography_level": "region", + "geography_id": "E12000003" + }, + { + "geography_level": "region", + "geography_id": "E12000004" + }, + { + "geography_level": "region", + "geography_id": "E12000005" + }, + { + "geography_level": "region", + "geography_id": "E12000006" + }, + { + "geography_level": "region", + "geography_id": "E12000007" + }, + { + "geography_level": "region", + "geography_id": "E12000008" + }, + { + "geography_level": "region", + "geography_id": "E12000009" + }, + { + "geography_level": "country", + "geography_id": "W92000004" + }, + { + "geography_level": "country", + "geography_id": "S92000003" + }, + { + "geography_level": "country", + "geography_id": "N92000002" + } + ] }, "ons.population.age_30_39_by_region": { - "geography_level": "country", - "geography_id": "K02000001" + "geography_fanout": [ + { + "geography_level": "region", + "geography_id": "E12000001" + }, + { + "geography_level": "region", + "geography_id": "E12000002" + }, + { + "geography_level": "region", + "geography_id": "E12000003" + }, + { + "geography_level": "region", + "geography_id": "E12000004" + }, + { + "geography_level": "region", + "geography_id": "E12000005" + }, + { + "geography_level": "region", + "geography_id": "E12000006" + }, + { + "geography_level": "region", + "geography_id": "E12000007" + }, + { + "geography_level": "region", + "geography_id": "E12000008" + }, + { + "geography_level": "region", + "geography_id": "E12000009" + }, + { + "geography_level": "country", + "geography_id": "W92000004" + }, + { + "geography_level": "country", + "geography_id": "S92000003" + }, + { + "geography_level": "country", + "geography_id": "N92000002" + } + ] }, "ons.population.age_40_49_by_region": { - "geography_level": "country", - "geography_id": "K02000001" + "geography_fanout": [ + { + "geography_level": "region", + "geography_id": "E12000001" + }, + { + "geography_level": "region", + "geography_id": "E12000002" + }, + { + "geography_level": "region", + "geography_id": "E12000003" + }, + { + "geography_level": "region", + "geography_id": "E12000004" + }, + { + "geography_level": "region", + "geography_id": "E12000005" + }, + { + "geography_level": "region", + "geography_id": "E12000006" + }, + { + "geography_level": "region", + "geography_id": "E12000007" + }, + { + "geography_level": "region", + "geography_id": "E12000008" + }, + { + "geography_level": "region", + "geography_id": "E12000009" + }, + { + "geography_level": "country", + "geography_id": "W92000004" + }, + { + "geography_level": "country", + "geography_id": "S92000003" + }, + { + "geography_level": "country", + "geography_id": "N92000002" + } + ] }, "ons.population.age_50_59_by_region": { - "geography_level": "country", - "geography_id": "K02000001" + "geography_fanout": [ + { + "geography_level": "region", + "geography_id": "E12000001" + }, + { + "geography_level": "region", + "geography_id": "E12000002" + }, + { + "geography_level": "region", + "geography_id": "E12000003" + }, + { + "geography_level": "region", + "geography_id": "E12000004" + }, + { + "geography_level": "region", + "geography_id": "E12000005" + }, + { + "geography_level": "region", + "geography_id": "E12000006" + }, + { + "geography_level": "region", + "geography_id": "E12000007" + }, + { + "geography_level": "region", + "geography_id": "E12000008" + }, + { + "geography_level": "region", + "geography_id": "E12000009" + }, + { + "geography_level": "country", + "geography_id": "W92000004" + }, + { + "geography_level": "country", + "geography_id": "S92000003" + }, + { + "geography_level": "country", + "geography_id": "N92000002" + } + ] }, "ons.population.age_60_69_by_region": { - "geography_level": "country", - "geography_id": "K02000001" + "geography_fanout": [ + { + "geography_level": "region", + "geography_id": "E12000001" + }, + { + "geography_level": "region", + "geography_id": "E12000002" + }, + { + "geography_level": "region", + "geography_id": "E12000003" + }, + { + "geography_level": "region", + "geography_id": "E12000004" + }, + { + "geography_level": "region", + "geography_id": "E12000005" + }, + { + "geography_level": "region", + "geography_id": "E12000006" + }, + { + "geography_level": "region", + "geography_id": "E12000007" + }, + { + "geography_level": "region", + "geography_id": "E12000008" + }, + { + "geography_level": "region", + "geography_id": "E12000009" + }, + { + "geography_level": "country", + "geography_id": "W92000004" + }, + { + "geography_level": "country", + "geography_id": "S92000003" + }, + { + "geography_level": "country", + "geography_id": "N92000002" + } + ] }, "ons.population.age_70_79_by_region": { - "geography_level": "country", - "geography_id": "K02000001" + "geography_fanout": [ + { + "geography_level": "region", + "geography_id": "E12000001" + }, + { + "geography_level": "region", + "geography_id": "E12000002" + }, + { + "geography_level": "region", + "geography_id": "E12000003" + }, + { + "geography_level": "region", + "geography_id": "E12000004" + }, + { + "geography_level": "region", + "geography_id": "E12000005" + }, + { + "geography_level": "region", + "geography_id": "E12000006" + }, + { + "geography_level": "region", + "geography_id": "E12000007" + }, + { + "geography_level": "region", + "geography_id": "E12000008" + }, + { + "geography_level": "region", + "geography_id": "E12000009" + }, + { + "geography_level": "country", + "geography_id": "W92000004" + }, + { + "geography_level": "country", + "geography_id": "S92000003" + }, + { + "geography_level": "country", + "geography_id": "N92000002" + } + ] }, "ons.population.age_80_89_by_region": { - "geography_level": "country", - "geography_id": "K02000001" + "geography_fanout": [ + { + "geography_level": "region", + "geography_id": "E12000001" + }, + { + "geography_level": "region", + "geography_id": "E12000002" + }, + { + "geography_level": "region", + "geography_id": "E12000003" + }, + { + "geography_level": "region", + "geography_id": "E12000004" + }, + { + "geography_level": "region", + "geography_id": "E12000005" + }, + { + "geography_level": "region", + "geography_id": "E12000006" + }, + { + "geography_level": "region", + "geography_id": "E12000007" + }, + { + "geography_level": "region", + "geography_id": "E12000008" + }, + { + "geography_level": "region", + "geography_id": "E12000009" + }, + { + "geography_level": "country", + "geography_id": "W92000004" + }, + { + "geography_level": "country", + "geography_id": "S92000003" + }, + { + "geography_level": "country", + "geography_id": "N92000002" + } + ] }, "ons.population.female_0_4": { "geography_level": "country", @@ -603,72 +1035,396 @@ "geography_id": "K02000001" }, "voa.council_tax_stock.band_a": { - "geography_level": "country", - "geography_id": "E92000001" - }, - "scotgov.council_tax_stock.band_a": { - "geography_level": "country", - "geography_id": "S92000003" - }, - "voa.council_tax_stock.band_b": { - "geography_level": "country", - "geography_id": "E92000001" - }, - "scotgov.council_tax_stock.band_b": { - "geography_level": "country", + "geography_fanout": [ + { + "geography_level": "region", + "geography_id": "E12000001" + }, + { + "geography_level": "region", + "geography_id": "E12000002" + }, + { + "geography_level": "region", + "geography_id": "E12000003" + }, + { + "geography_level": "region", + "geography_id": "E12000004" + }, + { + "geography_level": "region", + "geography_id": "E12000005" + }, + { + "geography_level": "region", + "geography_id": "E12000006" + }, + { + "geography_level": "region", + "geography_id": "E12000007" + }, + { + "geography_level": "region", + "geography_id": "E12000008" + }, + { + "geography_level": "region", + "geography_id": "E12000009" + } + ] + }, + "scotgov.council_tax_stock.band_a": { + "geography_level": "country", "geography_id": "S92000003" }, - "voa.council_tax_stock.band_c": { + "voa.council_tax_stock.band_b": { + "geography_fanout": [ + { + "geography_level": "region", + "geography_id": "E12000001" + }, + { + "geography_level": "region", + "geography_id": "E12000002" + }, + { + "geography_level": "region", + "geography_id": "E12000003" + }, + { + "geography_level": "region", + "geography_id": "E12000004" + }, + { + "geography_level": "region", + "geography_id": "E12000005" + }, + { + "geography_level": "region", + "geography_id": "E12000006" + }, + { + "geography_level": "region", + "geography_id": "E12000007" + }, + { + "geography_level": "region", + "geography_id": "E12000008" + }, + { + "geography_level": "region", + "geography_id": "E12000009" + } + ] + }, + "scotgov.council_tax_stock.band_b": { "geography_level": "country", - "geography_id": "E92000001" + "geography_id": "S92000003" + }, + "voa.council_tax_stock.band_c": { + "geography_fanout": [ + { + "geography_level": "region", + "geography_id": "E12000001" + }, + { + "geography_level": "region", + "geography_id": "E12000002" + }, + { + "geography_level": "region", + "geography_id": "E12000003" + }, + { + "geography_level": "region", + "geography_id": "E12000004" + }, + { + "geography_level": "region", + "geography_id": "E12000005" + }, + { + "geography_level": "region", + "geography_id": "E12000006" + }, + { + "geography_level": "region", + "geography_id": "E12000007" + }, + { + "geography_level": "region", + "geography_id": "E12000008" + }, + { + "geography_level": "region", + "geography_id": "E12000009" + } + ] }, "scotgov.council_tax_stock.band_c": { "geography_level": "country", "geography_id": "S92000003" }, "voa.council_tax_stock.band_d": { - "geography_level": "country", - "geography_id": "E92000001" + "geography_fanout": [ + { + "geography_level": "region", + "geography_id": "E12000001" + }, + { + "geography_level": "region", + "geography_id": "E12000002" + }, + { + "geography_level": "region", + "geography_id": "E12000003" + }, + { + "geography_level": "region", + "geography_id": "E12000004" + }, + { + "geography_level": "region", + "geography_id": "E12000005" + }, + { + "geography_level": "region", + "geography_id": "E12000006" + }, + { + "geography_level": "region", + "geography_id": "E12000007" + }, + { + "geography_level": "region", + "geography_id": "E12000008" + }, + { + "geography_level": "region", + "geography_id": "E12000009" + } + ] }, "scotgov.council_tax_stock.band_d": { "geography_level": "country", "geography_id": "S92000003" }, "voa.council_tax_stock.band_e": { - "geography_level": "country", - "geography_id": "E92000001" + "geography_fanout": [ + { + "geography_level": "region", + "geography_id": "E12000001" + }, + { + "geography_level": "region", + "geography_id": "E12000002" + }, + { + "geography_level": "region", + "geography_id": "E12000003" + }, + { + "geography_level": "region", + "geography_id": "E12000004" + }, + { + "geography_level": "region", + "geography_id": "E12000005" + }, + { + "geography_level": "region", + "geography_id": "E12000006" + }, + { + "geography_level": "region", + "geography_id": "E12000007" + }, + { + "geography_level": "region", + "geography_id": "E12000008" + }, + { + "geography_level": "region", + "geography_id": "E12000009" + } + ] }, "scotgov.council_tax_stock.band_e": { "geography_level": "country", "geography_id": "S92000003" }, "voa.council_tax_stock.band_f": { - "geography_level": "country", - "geography_id": "E92000001" + "geography_fanout": [ + { + "geography_level": "region", + "geography_id": "E12000001" + }, + { + "geography_level": "region", + "geography_id": "E12000002" + }, + { + "geography_level": "region", + "geography_id": "E12000003" + }, + { + "geography_level": "region", + "geography_id": "E12000004" + }, + { + "geography_level": "region", + "geography_id": "E12000005" + }, + { + "geography_level": "region", + "geography_id": "E12000006" + }, + { + "geography_level": "region", + "geography_id": "E12000007" + }, + { + "geography_level": "region", + "geography_id": "E12000008" + }, + { + "geography_level": "region", + "geography_id": "E12000009" + } + ] }, "scotgov.council_tax_stock.band_f": { "geography_level": "country", "geography_id": "S92000003" }, "voa.council_tax_stock.band_g": { - "geography_level": "country", - "geography_id": "E92000001" + "geography_fanout": [ + { + "geography_level": "region", + "geography_id": "E12000001" + }, + { + "geography_level": "region", + "geography_id": "E12000002" + }, + { + "geography_level": "region", + "geography_id": "E12000003" + }, + { + "geography_level": "region", + "geography_id": "E12000004" + }, + { + "geography_level": "region", + "geography_id": "E12000005" + }, + { + "geography_level": "region", + "geography_id": "E12000006" + }, + { + "geography_level": "region", + "geography_id": "E12000007" + }, + { + "geography_level": "region", + "geography_id": "E12000008" + }, + { + "geography_level": "region", + "geography_id": "E12000009" + } + ] }, "scotgov.council_tax_stock.band_g": { "geography_level": "country", "geography_id": "S92000003" }, "voa.council_tax_stock.band_h": { - "geography_level": "country", - "geography_id": "E92000001" + "geography_fanout": [ + { + "geography_level": "region", + "geography_id": "E12000001" + }, + { + "geography_level": "region", + "geography_id": "E12000002" + }, + { + "geography_level": "region", + "geography_id": "E12000003" + }, + { + "geography_level": "region", + "geography_id": "E12000004" + }, + { + "geography_level": "region", + "geography_id": "E12000005" + }, + { + "geography_level": "region", + "geography_id": 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The x1.9 trace is uk-data PR #311 / issue uk-data#230: SPI covers only taxpayers with liability, and HMRC Property Rental Income Statistics show GBP 46.68bn versus SPI about GBP 24.5bn for 2020-21. 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The Scottish rows resolve with their country-level geography and band pin; the English rows fan out over the nine English regions of the region tier (microcosm#905), each cell resolving VOA's region-stamped band count with the same band pin." + }, + { + "family": "ons_population", + "status": "active_region_tier_fanout", + "active_reference_count": 108, + "signed_rationale": "The nine ONS population-by-age-band targets fan out over the twelve-area region tier (nine English regions at Chronicle's region level, Wales, Scotland and Northern Ireland at country level), one reference per area, each scoped on the spine by a household-region predicate and placed at the region grain of the cross-grain rule (microcosm#905). The former single UK-wide row per band is retired: the tier sums to it within the same publication." } ], "signed_exclusion_rationales": [ diff --git a/packages/microcosm-build/src/microcosm/build/uk/target_references.json b/packages/microcosm-build/src/microcosm/build/uk/target_references.json index 5d947b470..ed173540f 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/target_references.json +++ b/packages/microcosm-build/src/microcosm/build/uk/target_references.json @@ -8023,7 +8023,7 @@ "uprating_to_period": 2025 }, { - "name": "ons.population.age_0_9_by_region", + "name": "ons.population.age_0_9_by_region@E12000001", "ledger_selector": { "source_name": "ons", "source_concept": "ons.mid_year_population_estimate", @@ -8042,19 +8042,23 @@ 9 ] }, - "geography_level": "country", - "geography_id": "K02000001", + "geography_level": "region", + "geography_id": "E12000001", "dimensions": [ "age" ] }, "entity": "person", - "measure": "ons/regional_age/0_9", + "measure": 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"ledger_selector": { "source_name": "ons", "source_concept": "ons.mid_year_population_estimate", "groupby_dimension": "age", "dimension_values": { "age": [ - 20, - 21, - 22, - 23, - 24, - 25, - 26, - 27, - 28, - 29 + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9 ] }, - "geography_level": "country", - "geography_id": "K02000001", + "geography_level": "region", + "geography_id": "E12000003", "dimensions": [ "age" ] }, "entity": "person", - "measure": "ons/regional_age/20_29", + "measure": "ons.population.age_0_9_by_region@E12000003", "family": "ons_population", "period": 2025, "metadata": { - "contract_target_id": "ons.population.age_20_29_by_region", - "measure_kind": "prepared_column" + "contract_target_id": "ons.population.age_0_9_by_region", + "measure_kind": "prepared_column", + "geography_level": "region", + "geography_id": "E12000003", + "geography_predicate": 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"cross_grain_grain": "region" }, "value_operation": "sum", "expected_member_count": 10, @@ -8257,38 +8281,42 @@ "uprating_to_period": 2025 }, { - "name": "ons.population.age_60_69_by_region", + "name": "ons.population.age_0_9_by_region@E12000007", "ledger_selector": { "source_name": "ons", "source_concept": "ons.mid_year_population_estimate", "groupby_dimension": "age", "dimension_values": { "age": [ - 60, - 61, - 62, - 63, - 64, - 65, - 66, - 67, - 68, - 69 + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9 ] }, - "geography_level": "country", - "geography_id": "K02000001", + "geography_level": "region", + "geography_id": "E12000007", "dimensions": [ "age" ] }, "entity": "person", - "measure": "ons/regional_age/60_69", + "measure": "ons.population.age_0_9_by_region@E12000007", "family": "ons_population", "period": 2025, "metadata": { - "contract_target_id": "ons.population.age_60_69_by_region", - "measure_kind": "prepared_column" + "contract_target_id": "ons.population.age_0_9_by_region", + "measure_kind": "prepared_column", + "geography_level": "region", + "geography_id": "E12000007", + "geography_predicate": "{\"entity\":\"household\",\"map_to\":\"person\",\"operator\":\"==\",\"reduce\":\"any\",\"value\":\"LONDON\",\"variable\":\"region\"}", + "cross_grain_grain": "region" }, "value_operation": "sum", "expected_member_count": 10, @@ -8296,38 +8324,42 @@ "uprating_to_period": 2025 }, { - "name": "ons.population.age_70_79_by_region", + "name": "ons.population.age_0_9_by_region@E12000008", "ledger_selector": { "source_name": "ons", "source_concept": "ons.mid_year_population_estimate", "groupby_dimension": "age", "dimension_values": { "age": [ - 70, - 71, - 72, - 73, - 74, - 75, - 76, - 77, - 78, - 79 + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9 ] }, - "geography_level": "country", - "geography_id": "K02000001", + "geography_level": "region", + "geography_id": "E12000008", "dimensions": [ "age" ] }, "entity": "person", - "measure": "ons/regional_age/70_79", + "measure": "ons.population.age_0_9_by_region@E12000008", "family": "ons_population", "period": 2025, "metadata": { - "contract_target_id": "ons.population.age_70_79_by_region", - "measure_kind": "prepared_column" + "contract_target_id": "ons.population.age_0_9_by_region", + "measure_kind": "prepared_column", + "geography_level": "region", + "geography_id": "E12000008", + "geography_predicate": "{\"entity\":\"household\",\"map_to\":\"person\",\"operator\":\"==\",\"reduce\":\"any\",\"value\":\"SOUTH_EAST\",\"variable\":\"region\"}", + "cross_grain_grain": "region" }, "value_operation": "sum", "expected_member_count": 10, @@ -8335,38 +8367,42 @@ "uprating_to_period": 2025 }, { - "name": "ons.population.age_80_89_by_region", + "name": "ons.population.age_0_9_by_region@E12000009", "ledger_selector": { "source_name": "ons", "source_concept": "ons.mid_year_population_estimate", "groupby_dimension": "age", "dimension_values": { "age": [ - 80, - 81, - 82, - 83, - 84, 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"voa.council_tax_stock.band_c", + "name": "voa.council_tax_stock.band_h@E12000007", "ledger_selector": { "source_name": "voa", - "source_measure_id": "band_c", - "geography_level": "country", - "geography_id": "E92000001" + "source_measure_id": "band_h", + "geography_level": "region", + "geography_id": "E12000007" }, "entity": "household", - "measure": "voa/council_tax/band_c", + "measure": "voa.council_tax_stock.band_h@E12000007", "family": "council_tax_stock", "period": 2025, "metadata": { - "contract_target_id": "voa.council_tax_stock.band_c", - "measure_kind": "prepared_column" + "contract_target_id": "voa.council_tax_stock.band_h", + "measure_kind": "prepared_column", + "geography_level": "region", + "geography_id": "E12000007", + "geography_predicate": "{\"entity\":\"household\",\"map_to\":\"household\",\"operator\":\"==\",\"reduce\":\"any\",\"value\":\"LONDON\",\"variable\":\"region\"}", + "cross_grain_grain": "region" }, "uprating_from_period": "2025-03", "uprating_to_period": 2025 }, { - "name": "scotgov.council_tax_stock.band_c", + "name": "voa.council_tax_stock.band_h@E12000008", "ledger_selector": { - "source_name": "scotgov", - "source_measure_id": "band_c", - "geography_level": "country", - "geography_id": "S92000003" + "source_name": "voa", + "source_measure_id": "band_h", + "geography_level": "region", + "geography_id": "E12000008" }, "entity": "household", - "measure": "voa/council_tax/SCOTLAND/band_c", + "measure": "voa.council_tax_stock.band_h@E12000008", "family": "council_tax_stock", "period": 2025, "metadata": { - "contract_target_id": "scotgov.council_tax_stock.band_c", - "measure_kind": "prepared_column" + "contract_target_id": "voa.council_tax_stock.band_h", + "measure_kind": "prepared_column", + "geography_level": "region", + "geography_id": "E12000008", + "geography_predicate": "{\"entity\":\"household\",\"map_to\":\"household\",\"operator\":\"==\",\"reduce\":\"any\",\"value\":\"SOUTH_EAST\",\"variable\":\"region\"}", + "cross_grain_grain": "region" }, - "uprating_from_period": "2025-09", + "uprating_from_period": "2025-03", "uprating_to_period": 2025 }, { - "name": "voa.council_tax_stock.band_d", + "name": "voa.council_tax_stock.band_h@E12000009", "ledger_selector": { "source_name": "voa", - "source_measure_id": "band_d", - "geography_level": "country", - "geography_id": "E92000001" + "source_measure_id": "band_h", + "geography_level": "region", + "geography_id": "E12000009" }, "entity": "household", - "measure": "voa/council_tax/band_d", + "measure": "voa.council_tax_stock.band_h@E12000009", "family": "council_tax_stock", "period": 2025, "metadata": { - "contract_target_id": "voa.council_tax_stock.band_d", - "measure_kind": "prepared_column" + "contract_target_id": "voa.council_tax_stock.band_h", + "measure_kind": "prepared_column", + "geography_level": "region", + "geography_id": "E12000009", + "geography_predicate": "{\"entity\":\"household\",\"map_to\":\"household\",\"operator\":\"==\",\"reduce\":\"any\",\"value\":\"SOUTH_WEST\",\"variable\":\"region\"}", + "cross_grain_grain": "region" }, "uprating_from_period": "2025-03", "uprating_to_period": 2025 }, { - "name": "scotgov.council_tax_stock.band_d", + "name": "scotgov.council_tax_stock.band_h", "ledger_selector": { "source_name": "scotgov", - "source_measure_id": "band_d", + "source_measure_id": "band_h", "geography_level": "country", "geography_id": "S92000003" }, "entity": "household", - "measure": "voa/council_tax/SCOTLAND/band_d", + "measure": "voa/council_tax/SCOTLAND/band_h", "family": "council_tax_stock", "period": 2025, "metadata": { - "contract_target_id": "scotgov.council_tax_stock.band_d", + "contract_target_id": "scotgov.council_tax_stock.band_h", "measure_kind": "prepared_column" }, "uprating_from_period": "2025-09", "uprating_to_period": 2025 }, { - "name": "voa.council_tax_stock.band_e", + "name": "voa.council_tax_stock.total@E12000001", "ledger_selector": { "source_name": "voa", - "source_measure_id": "band_e", - "geography_level": "country", - "geography_id": "E92000001" + "source_measure_id": "all_properties", + "geography_level": "region", + "geography_id": "E12000001" }, "entity": "household", - "measure": "voa/council_tax/band_e", + "measure": "voa.council_tax_stock.total@E12000001", "family": "council_tax_stock", "period": 2025, "metadata": { - "contract_target_id": "voa.council_tax_stock.band_e", - "measure_kind": "prepared_column" + "contract_target_id": "voa.council_tax_stock.total", + "measure_kind": "prepared_column", + "geography_level": "region", + "geography_id": "E12000001", + "geography_predicate": "{\"entity\":\"household\",\"map_to\":\"household\",\"operator\":\"==\",\"reduce\":\"any\",\"value\":\"NORTH_EAST\",\"variable\":\"region\"}", + "cross_grain_grain": "region" }, "uprating_from_period": "2025-03", "uprating_to_period": 2025 }, { - "name": "scotgov.council_tax_stock.band_e", + "name": "voa.council_tax_stock.total@E12000002", "ledger_selector": { - "source_name": "scotgov", - "source_measure_id": "band_e", - "geography_level": "country", - "geography_id": "S92000003" + "source_name": "voa", + "source_measure_id": "all_properties", + "geography_level": "region", + "geography_id": "E12000002" }, "entity": "household", - "measure": "voa/council_tax/SCOTLAND/band_e", + "measure": "voa.council_tax_stock.total@E12000002", "family": "council_tax_stock", "period": 2025, "metadata": { - "contract_target_id": "scotgov.council_tax_stock.band_e", - "measure_kind": "prepared_column" + "contract_target_id": "voa.council_tax_stock.total", + "measure_kind": "prepared_column", + "geography_level": "region", + "geography_id": "E12000002", + "geography_predicate": "{\"entity\":\"household\",\"map_to\":\"household\",\"operator\":\"==\",\"reduce\":\"any\",\"value\":\"NORTH_WEST\",\"variable\":\"region\"}", + "cross_grain_grain": "region" }, - "uprating_from_period": "2025-09", + "uprating_from_period": "2025-03", "uprating_to_period": 2025 }, { - "name": "voa.council_tax_stock.band_f", + "name": "voa.council_tax_stock.total@E12000003", "ledger_selector": { "source_name": "voa", - "source_measure_id": "band_f", - "geography_level": "country", - "geography_id": "E92000001" + "source_measure_id": "all_properties", + "geography_level": "region", + "geography_id": "E12000003" }, "entity": "household", - "measure": "voa/council_tax/band_f", + "measure": "voa.council_tax_stock.total@E12000003", "family": "council_tax_stock", "period": 2025, "metadata": { - "contract_target_id": "voa.council_tax_stock.band_f", - "measure_kind": "prepared_column" + "contract_target_id": "voa.council_tax_stock.total", + "measure_kind": "prepared_column", + "geography_level": "region", + "geography_id": "E12000003", + "geography_predicate": "{\"entity\":\"household\",\"map_to\":\"household\",\"operator\":\"==\",\"reduce\":\"any\",\"value\":\"YORKSHIRE\",\"variable\":\"region\"}", + "cross_grain_grain": "region" }, "uprating_from_period": "2025-03", "uprating_to_period": 2025 }, { - "name": "scotgov.council_tax_stock.band_f", + "name": "voa.council_tax_stock.total@E12000004", "ledger_selector": { - "source_name": "scotgov", - "source_measure_id": "band_f", - "geography_level": "country", - "geography_id": "S92000003" + "source_name": "voa", + "source_measure_id": "all_properties", + "geography_level": "region", + "geography_id": "E12000004" }, "entity": "household", - "measure": "voa/council_tax/SCOTLAND/band_f", + "measure": "voa.council_tax_stock.total@E12000004", "family": "council_tax_stock", "period": 2025, "metadata": { - "contract_target_id": "scotgov.council_tax_stock.band_f", - "measure_kind": "prepared_column" + "contract_target_id": "voa.council_tax_stock.total", + "measure_kind": "prepared_column", + "geography_level": "region", + "geography_id": "E12000004", + "geography_predicate": "{\"entity\":\"household\",\"map_to\":\"household\",\"operator\":\"==\",\"reduce\":\"any\",\"value\":\"EAST_MIDLANDS\",\"variable\":\"region\"}", + "cross_grain_grain": "region" }, - "uprating_from_period": "2025-09", + "uprating_from_period": "2025-03", "uprating_to_period": 2025 }, { - "name": "voa.council_tax_stock.band_g", + "name": "voa.council_tax_stock.total@E12000005", "ledger_selector": { "source_name": "voa", - "source_measure_id": "band_g", - "geography_level": "country", - "geography_id": "E92000001" + "source_measure_id": "all_properties", + "geography_level": "region", + "geography_id": "E12000005" }, "entity": "household", - "measure": "voa/council_tax/band_g", + "measure": "voa.council_tax_stock.total@E12000005", "family": "council_tax_stock", "period": 2025, "metadata": { - "contract_target_id": "voa.council_tax_stock.band_g", - "measure_kind": "prepared_column" + "contract_target_id": "voa.council_tax_stock.total", + "measure_kind": "prepared_column", + "geography_level": "region", + "geography_id": "E12000005", + "geography_predicate": "{\"entity\":\"household\",\"map_to\":\"household\",\"operator\":\"==\",\"reduce\":\"any\",\"value\":\"WEST_MIDLANDS\",\"variable\":\"region\"}", + "cross_grain_grain": "region" }, "uprating_from_period": "2025-03", "uprating_to_period": 2025 }, { - "name": "scotgov.council_tax_stock.band_g", + "name": "voa.council_tax_stock.total@E12000006", "ledger_selector": { - "source_name": "scotgov", - "source_measure_id": "band_g", - "geography_level": "country", - "geography_id": "S92000003" + "source_name": "voa", + "source_measure_id": "all_properties", + "geography_level": "region", + "geography_id": "E12000006" }, "entity": "household", - "measure": "voa/council_tax/SCOTLAND/band_g", + "measure": "voa.council_tax_stock.total@E12000006", "family": "council_tax_stock", "period": 2025, "metadata": { - "contract_target_id": "scotgov.council_tax_stock.band_g", - "measure_kind": "prepared_column" + "contract_target_id": "voa.council_tax_stock.total", + "measure_kind": "prepared_column", + "geography_level": "region", + "geography_id": "E12000006", + "geography_predicate": "{\"entity\":\"household\",\"map_to\":\"household\",\"operator\":\"==\",\"reduce\":\"any\",\"value\":\"EAST_OF_ENGLAND\",\"variable\":\"region\"}", + "cross_grain_grain": "region" }, - "uprating_from_period": "2025-09", + "uprating_from_period": "2025-03", "uprating_to_period": 2025 }, { - "name": "voa.council_tax_stock.band_h", + "name": "voa.council_tax_stock.total@E12000007", "ledger_selector": { "source_name": "voa", - "source_measure_id": "band_h", - "geography_level": "country", - "geography_id": "E92000001" + "source_measure_id": "all_properties", + "geography_level": "region", + "geography_id": "E12000007" }, "entity": "household", - "measure": "voa/council_tax/band_h", + "measure": "voa.council_tax_stock.total@E12000007", "family": "council_tax_stock", "period": 2025, "metadata": { - "contract_target_id": "voa.council_tax_stock.band_h", - "measure_kind": "prepared_column" + "contract_target_id": "voa.council_tax_stock.total", + "measure_kind": "prepared_column", + "geography_level": "region", + "geography_id": "E12000007", + "geography_predicate": "{\"entity\":\"household\",\"map_to\":\"household\",\"operator\":\"==\",\"reduce\":\"any\",\"value\":\"LONDON\",\"variable\":\"region\"}", + "cross_grain_grain": "region" }, "uprating_from_period": "2025-03", "uprating_to_period": 2025 }, { - "name": "scotgov.council_tax_stock.band_h", + "name": "voa.council_tax_stock.total@E12000008", "ledger_selector": { - "source_name": "scotgov", - "source_measure_id": "band_h", - "geography_level": "country", - "geography_id": "S92000003" + "source_name": "voa", + "source_measure_id": "all_properties", + "geography_level": "region", + "geography_id": "E12000008" }, "entity": "household", - "measure": "voa/council_tax/SCOTLAND/band_h", + "measure": "voa.council_tax_stock.total@E12000008", "family": "council_tax_stock", "period": 2025, "metadata": { - "contract_target_id": "scotgov.council_tax_stock.band_h", - "measure_kind": "prepared_column" + "contract_target_id": "voa.council_tax_stock.total", + "measure_kind": "prepared_column", + "geography_level": "region", + "geography_id": "E12000008", + "geography_predicate": "{\"entity\":\"household\",\"map_to\":\"household\",\"operator\":\"==\",\"reduce\":\"any\",\"value\":\"SOUTH_EAST\",\"variable\":\"region\"}", + "cross_grain_grain": "region" }, - "uprating_from_period": "2025-09", + "uprating_from_period": "2025-03", "uprating_to_period": 2025 }, { - "name": "voa.council_tax_stock.total", + "name": "voa.council_tax_stock.total@E12000009", "ledger_selector": { "source_name": "voa", "source_measure_id": "all_properties", - "geography_level": "country", - "geography_id": "E92000001" + "geography_level": "region", + "geography_id": "E12000009" }, "entity": "household", - "measure": "voa/council_tax/total", + "measure": "voa.council_tax_stock.total@E12000009", "family": "council_tax_stock", "period": 2025, "metadata": { "contract_target_id": "voa.council_tax_stock.total", - "measure_kind": "prepared_column" + "measure_kind": "prepared_column", + "geography_level": "region", + "geography_id": "E12000009", + "geography_predicate": "{\"entity\":\"household\",\"map_to\":\"household\",\"operator\":\"==\",\"reduce\":\"any\",\"value\":\"SOUTH_WEST\",\"variable\":\"region\"}", + "cross_grain_grain": "region" }, "uprating_from_period": "2025-03", "uprating_to_period": 2025 diff --git a/packages/microcosm-build/src/microcosm/build/uk/uk_population_targets.json b/packages/microcosm-build/src/microcosm/build/uk/uk_population_targets.json index 3ec0b3eb6..f3255a368 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/uk_population_targets.json +++ b/packages/microcosm-build/src/microcosm/build/uk/uk_population_targets.json @@ -6078,7 +6078,7 @@ "value_variable": "person_count", "from_entity": "person", "map_to": "household", - "notes": "One row per geography: English regions resolve at region level, Wales/Scotland/Northern Ireland at country level; the resolved geography binds the model region filter (compute/demographics.py:22-38)." + "notes": "One reference per region-tier area (microcosm#905): the nine English regions at Chronicle's region level, Wales, Scotland and Northern Ireland at country level. Each cell is scoped on the spine by the household-region predicate the generator stamps into its reference metadata (geography_predicate), projected to the person entity, and sits at the region grain of the cross-grain rule as the control for its constituencies and authorities." }, "axiom": { "metric_name": "ons/regional_age/0_9", @@ -6148,7 +6148,7 @@ "value_variable": "person_count", "from_entity": "person", "map_to": "household", - "notes": "One row per geography: English regions resolve at region level, Wales/Scotland/Northern Ireland at country level; the resolved geography binds the model region filter (compute/demographics.py:22-38)." + "notes": "One reference per region-tier area (microcosm#905): the nine English regions at Chronicle's region level, Wales, Scotland and Northern Ireland at country level. Each cell is scoped on the spine by the household-region predicate the generator stamps into its reference metadata (geography_predicate), projected to the person entity, and sits at the region grain of the cross-grain rule as the control for its constituencies and authorities." }, "axiom": { "metric_name": "ons/regional_age/10_19", @@ -6218,7 +6218,7 @@ "value_variable": "person_count", "from_entity": "person", "map_to": "household", - "notes": "One row per geography: English regions resolve at region level, Wales/Scotland/Northern Ireland at country level; the resolved geography binds the model region filter (compute/demographics.py:22-38)." + "notes": "One reference per region-tier area (microcosm#905): the nine English regions at Chronicle's region level, Wales, Scotland and Northern Ireland at country level. Each cell is scoped on the spine by the household-region predicate the generator stamps into its reference metadata (geography_predicate), projected to the person entity, and sits at the region grain of the cross-grain rule as the control for its constituencies and authorities." }, "axiom": { "metric_name": "ons/regional_age/20_29", @@ -6288,7 +6288,7 @@ "value_variable": "person_count", "from_entity": "person", "map_to": "household", - "notes": "One row per geography: English regions resolve at region level, Wales/Scotland/Northern Ireland at country level; the resolved geography binds the model region filter (compute/demographics.py:22-38)." + "notes": "One reference per region-tier area (microcosm#905): the nine English regions at Chronicle's region level, Wales, Scotland and Northern Ireland at country level. Each cell is scoped on the spine by the household-region predicate the generator stamps into its reference metadata (geography_predicate), projected to the person entity, and sits at the region grain of the cross-grain rule as the control for its constituencies and authorities." }, "axiom": { "metric_name": "ons/regional_age/30_39", @@ -6358,7 +6358,7 @@ "value_variable": "person_count", "from_entity": "person", "map_to": "household", - "notes": "One row per geography: English regions resolve at region level, Wales/Scotland/Northern Ireland at country level; the resolved geography binds the model region filter (compute/demographics.py:22-38)." + "notes": "One reference per region-tier area (microcosm#905): the nine English regions at Chronicle's region level, Wales, Scotland and Northern Ireland at country level. Each cell is scoped on the spine by the household-region predicate the generator stamps into its reference metadata (geography_predicate), projected to the person entity, and sits at the region grain of the cross-grain rule as the control for its constituencies and authorities." }, "axiom": { "metric_name": "ons/regional_age/40_49", @@ -6428,7 +6428,7 @@ "value_variable": "person_count", "from_entity": "person", "map_to": "household", - "notes": "One row per geography: English regions resolve at region level, Wales/Scotland/Northern Ireland at country level; the resolved geography binds the model region filter (compute/demographics.py:22-38)." + "notes": "One reference per region-tier area (microcosm#905): the nine English regions at Chronicle's region level, Wales, Scotland and Northern Ireland at country level. Each cell is scoped on the spine by the household-region predicate the generator stamps into its reference metadata (geography_predicate), projected to the person entity, and sits at the region grain of the cross-grain rule as the control for its constituencies and authorities." }, "axiom": { "metric_name": "ons/regional_age/50_59", @@ -6498,7 +6498,7 @@ "value_variable": "person_count", "from_entity": "person", "map_to": "household", - "notes": "One row per geography: English regions resolve at region level, Wales/Scotland/Northern Ireland at country level; the resolved geography binds the model region filter (compute/demographics.py:22-38)." + "notes": "One reference per region-tier area (microcosm#905): the nine English regions at Chronicle's region level, Wales, Scotland and Northern Ireland at country level. Each cell is scoped on the spine by the household-region predicate the generator stamps into its reference metadata (geography_predicate), projected to the person entity, and sits at the region grain of the cross-grain rule as the control for its constituencies and authorities." }, "axiom": { "metric_name": "ons/regional_age/60_69", @@ -6568,7 +6568,7 @@ "value_variable": "person_count", "from_entity": "person", "map_to": "household", - "notes": "One row per geography: English regions resolve at region level, Wales/Scotland/Northern Ireland at country level; the resolved geography binds the model region filter (compute/demographics.py:22-38)." + "notes": "One reference per region-tier area (microcosm#905): the nine English regions at Chronicle's region level, Wales, Scotland and Northern Ireland at country level. Each cell is scoped on the spine by the household-region predicate the generator stamps into its reference metadata (geography_predicate), projected to the person entity, and sits at the region grain of the cross-grain rule as the control for its constituencies and authorities." }, "axiom": { "metric_name": "ons/regional_age/70_79", @@ -6638,7 +6638,7 @@ "value_variable": "person_count", "from_entity": "person", "map_to": "household", - "notes": "One row per geography: English regions resolve at region level, Wales/Scotland/Northern Ireland at country level; the resolved geography binds the model region filter (compute/demographics.py:22-38)." + "notes": "One reference per region-tier area (microcosm#905): the nine English regions at Chronicle's region level, Wales, Scotland and Northern Ireland at country level. Each cell is scoped on the spine by the household-region predicate the generator stamps into its reference metadata (geography_predicate), projected to the person entity, and sits at the region grain of the cross-grain rule as the control for its constituencies and authorities." }, "axiom": { "metric_name": "ons/regional_age/80_89", @@ -10113,7 +10113,7 @@ } ], "value_variable": "household_count", - "notes": "England dwelling stock proxied by households. National references select E92000001, and this binding explicitly filters country == ENGLAND. Local VOA bands use the separate by_area bindings." + "notes": "England dwelling stock proxied by households. National references fan out over the nine English regions (microcosm#905), each cell scoped by the household-region predicate in its reference metadata, and this binding explicitly filters country == ENGLAND. Local VOA bands use the separate by_area bindings." }, "axiom": { "metric_name": "voa/council_tax/band_a", @@ -10206,7 +10206,7 @@ } ], "value_variable": "household_count", - "notes": "England dwelling stock proxied by households. National references select E92000001, and this binding explicitly filters country == ENGLAND. Local VOA bands use the separate by_area bindings." + "notes": "England dwelling stock proxied by households. National references fan out over the nine English regions (microcosm#905), each cell scoped by the household-region predicate in its reference metadata, and this binding explicitly filters country == ENGLAND. Local VOA bands use the separate by_area bindings." }, "axiom": { "metric_name": "voa/council_tax/band_b", @@ -10299,7 +10299,7 @@ } ], "value_variable": "household_count", - "notes": "England dwelling stock proxied by households. National references select E92000001, and this binding explicitly filters country == ENGLAND. Local VOA bands use the separate by_area bindings." + "notes": "England dwelling stock proxied by households. National references fan out over the nine English regions (microcosm#905), each cell scoped by the household-region predicate in its reference metadata, and this binding explicitly filters country == ENGLAND. Local VOA bands use the separate by_area bindings." }, "axiom": { "metric_name": "voa/council_tax/band_c", @@ -10392,7 +10392,7 @@ } ], "value_variable": "household_count", - "notes": "England dwelling stock proxied by households. National references select E92000001, and this binding explicitly filters country == ENGLAND. Local VOA bands use the separate by_area bindings." + "notes": "England dwelling stock proxied by households. National references fan out over the nine English regions (microcosm#905), each cell scoped by the household-region predicate in its reference metadata, and this binding explicitly filters country == ENGLAND. Local VOA bands use the separate by_area bindings." }, "axiom": { "metric_name": "voa/council_tax/band_d", @@ -10485,7 +10485,7 @@ } ], "value_variable": "household_count", - "notes": "England dwelling stock proxied by households. National references select E92000001, and this binding explicitly filters country == ENGLAND. Local VOA bands use the separate by_area bindings." + "notes": "England dwelling stock proxied by households. National references fan out over the nine English regions (microcosm#905), each cell scoped by the household-region predicate in its reference metadata, and this binding explicitly filters country == ENGLAND. Local VOA bands use the separate by_area bindings." }, "axiom": { "metric_name": "voa/council_tax/band_e", @@ -10578,7 +10578,7 @@ } ], "value_variable": "household_count", - "notes": "England dwelling stock proxied by households. National references select E92000001, and this binding explicitly filters country == ENGLAND. Local VOA bands use the separate by_area bindings." + "notes": "England dwelling stock proxied by households. National references fan out over the nine English regions (microcosm#905), each cell scoped by the household-region predicate in its reference metadata, and this binding explicitly filters country == ENGLAND. Local VOA bands use the separate by_area bindings." }, "axiom": { "metric_name": "voa/council_tax/band_f", @@ -10671,7 +10671,7 @@ } ], "value_variable": "household_count", - "notes": "England dwelling stock proxied by households. National references select E92000001, and this binding explicitly filters country == ENGLAND. Local VOA bands use the separate by_area bindings." + "notes": "England dwelling stock proxied by households. National references fan out over the nine English regions (microcosm#905), each cell scoped by the household-region predicate in its reference metadata, and this binding explicitly filters country == ENGLAND. Local VOA bands use the separate by_area bindings." }, "axiom": { "metric_name": "voa/council_tax/band_g", @@ -10764,7 +10764,7 @@ } ], "value_variable": "household_count", - "notes": "England dwelling stock proxied by households. National references select E92000001, and this binding explicitly filters country == ENGLAND. Local VOA bands use the separate by_area bindings." + "notes": "England dwelling stock proxied by households. National references fan out over the nine English regions (microcosm#905), each cell scoped by the household-region predicate in its reference metadata, and this binding explicitly filters country == ENGLAND. Local VOA bands use the separate by_area bindings." }, "axiom": { "metric_name": "voa/council_tax/band_h", @@ -10851,7 +10851,7 @@ } ], "value_variable": "household_count", - "notes": "England dwelling stock proxied by households. National references select E92000001, and this binding explicitly filters country == ENGLAND. Local VOA bands use the separate by_area bindings." + "notes": "England dwelling stock proxied by households. National references fan out over the nine English regions (microcosm#905), each cell scoped by the household-region predicate in its reference metadata, and this binding explicitly filters country == ENGLAND. Local VOA bands use the separate by_area bindings." }, "axiom": { "metric_name": "voa/council_tax/total", diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/__init__.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/__init__.py index 187310e40..4a57e2f06 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/__init__.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/__init__.py @@ -154,6 +154,9 @@ UK_ENGLAND_WALES_REGION_CODES, UK_GEOGRAPHY_LADDER_COLUMNS, UK_LONDON_REGION_CODE, + UK_LADDER_NATION_REGION_CODES, + UK_REGION_TIER, + UK_REGION_TIER_ENUM, UK_OA_LADDER_DERIVED_LAYERS, UK_OA_LADDER_KIND, UK_OA_LADDER_SCHEMA_VERSION, @@ -712,6 +715,9 @@ "UK_GEOGRAPHY_LADDER_COLUMNS", "UK_HMRC_INCOME_SOURCE_STAGES_RESOURCE", "UK_LONDON_REGION_CODE", + "UK_LADDER_NATION_REGION_CODES", + "UK_REGION_TIER", + "UK_REGION_TIER_ENUM", "UK_LOADER_INPUT_ALIASES", "UK_OA_LADDER_DERIVED_LAYERS", "UK_OA_LADDER_KIND", diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/geography_ladder.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/geography_ladder.py index b5015c4b1..9c028ca38 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/geography_ladder.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/geography_ladder.py @@ -143,6 +143,55 @@ #: collapse to zero is caught the way the US ladder catches an NYC collapse). UK_LONDON_REGION_CODE = "E12000007" +#: The twelve-area region tier of the national calibration surface, in the +#: order the FRS ``gvtregno`` coding and the incumbent's regional rows use: +#: the nine English regions at Chronicle's ``region`` level, then Wales, +#: Scotland and Northern Ireland, which Chronicle stamps at ``country`` (their +#: GSS codes are country codes) but which sit at the same ITL1 tier as the +#: English regions. Two-level (country + region) contract targets fan out +#: over this roster (microcosm#905). +UK_REGION_TIER: tuple[tuple[str, str], ...] = ( + ("region", "E12000001"), # North East + ("region", "E12000002"), # North West + ("region", "E12000003"), # Yorkshire and The Humber + ("region", "E12000004"), # East Midlands + ("region", "E12000005"), # West Midlands + ("region", "E12000006"), # East of England + ("region", "E12000007"), # London + ("region", "E12000008"), # South East + ("region", "E12000009"), # South West + ("country", "W92000004"), # Wales + ("country", "S92000003"), # Scotland + ("country", "N92000002"), # Northern Ireland +) + +#: Region-tier GSS code -> the spine's ``region`` enum name (``REGION_MAP`` in +#: ``frs_spine``), the value a fan-out row's geography predicate compares +#: against. Kept beside the roster so the two cannot drift apart. +UK_REGION_TIER_ENUM: dict[str, str] = { + "E12000001": "NORTH_EAST", + "E12000002": "NORTH_WEST", + "E12000003": "YORKSHIRE", + "E12000004": "EAST_MIDLANDS", + "E12000005": "WEST_MIDLANDS", + "E12000006": "EAST_OF_ENGLAND", + "E12000007": "LONDON", + "E12000008": "SOUTH_EAST", + "E12000009": "SOUTH_WEST", + "W92000004": "WALES", + "S92000003": "SCOTLAND", + "N92000002": "NORTHERN_IRELAND", +} + +#: The ladder's nation pseudo region codes -> the GSS country code the region +#: tier uses for the same area, so an OA-derived region membership can be +#: expressed in tier codes. +UK_LADDER_NATION_REGION_CODES: dict[str, str] = { + "W99999999": "W92000004", + "S99999999": "S92000003", + "N99999999": "N92000002", +} + UK_OA_LADDER_SCHEMA_VERSION = 1 UK_OA_LADDER_KIND = "uk_oa_ladder" @@ -453,10 +502,7 @@ def expected_uk_ladder_area_support( region_households = float(constituency_weight.sum()) for constituency_code, household_count in constituency_weight.items(): expected_rows = ( - n_clones - * int(n_region) - * float(household_count) - / region_households + n_clones * int(n_region) * float(household_count) / region_households ) constituency_key = str(constituency_code) constituency_expected[constituency_key] += expected_rows @@ -504,9 +550,13 @@ def uk_region_mix( """Summarize household row and weight shares by normalized UK region.""" if region_column not in household.columns: - raise ValueError(f"household table must contain region column {region_column!r}.") + raise ValueError( + f"household table must contain region column {region_column!r}." + ) if weight_column not in household.columns: - raise ValueError(f"household table must contain weight column {weight_column!r}.") + raise ValueError( + f"household table must contain weight column {weight_column!r}." + ) region_codes = _household_region_codes( household[region_column], @@ -518,7 +568,9 @@ def uk_region_mix( if not np.isfinite(weights).all() or (weights < 0).any(): raise ValueError(f"{weight_column} must be finite and non-negative.") if len(weights) == 0: - raise ValueError("household table must contain at least one row for region mix.") + raise ValueError( + "household table must contain at least one row for region mix." + ) total_weight = float(weights.sum()) if total_weight <= 0: raise ValueError(f"{weight_column} must carry positive total weight.") diff --git a/packages/microcosm-build/tests/test_country_spec.py b/packages/microcosm-build/tests/test_country_spec.py index 975d51fa2..2c03de478 100644 --- a/packages/microcosm-build/tests/test_country_spec.py +++ b/packages/microcosm-build/tests/test_country_spec.py @@ -1081,7 +1081,7 @@ def test_uk_target_references_accept_regenerated_contract_fields(self) -> None: spec = load_country_spec("uk") references = {reference.name: reference for reference in spec.target_references} - assert len(references) == 424 + assert len(references) == 595 # microcosm#905: 424 - 18 country rows + 189 region-tier cells assert references["obr.esa"].value_operation == "sum" assert references["dwp.uc.households"].value_operation == ( "monthly_window_sum_average" diff --git a/packages/microcosm-build/tests/test_target_reference_authoring.py b/packages/microcosm-build/tests/test_target_reference_authoring.py index 5b4b04595..4cef678e6 100644 --- a/packages/microcosm-build/tests/test_target_reference_authoring.py +++ b/packages/microcosm-build/tests/test_target_reference_authoring.py @@ -10,7 +10,9 @@ from microcosm.build.target_reference_authoring import ( AreaSignedDeferral, AreaTargetReferenceAuthoringConfig, + TargetReferenceAuthoringConfig, author_area_target_references, + author_target_references, ) @@ -476,3 +478,159 @@ def _area_fact( "measure_id": measure_id, }, } + + +_REGION_TIER_CELLS = ( + ("region", "E12000007"), + ("region", "E12000001"), + ("country", "W92000004"), +) + + +def _tier_fact(level: str, area_id: str, value: float, fact_key: str) -> dict: + fact = _area_fact("ons", "population", value, area_id=area_id, fact_key=fact_key) + fact["geography"] = {"level": level, "id": area_id, "vintage": "gss_2024"} + return fact + + +def _two_level_contract() -> dict: + contract = _single_age_contract() + target = contract["targets"][0] + target["target_id"] = "ons.age.0_10_by_region" + target["geography_levels"] = ["country", "region"] + return contract + + +def _tier_metadata( + target: dict, geography_level: str, geography_id: str, entity: str +) -> dict[str, str]: + del target, geography_level + return { + "geography_predicate": json.dumps( + { + "variable": "region", + "operator": "==", + "value": geography_id, + "map_to": entity, + } + ), + "cross_grain_grain": "region", + } + + +def _tier_facts() -> list[dict]: + return [ + _tier_fact("region", "E12000007", 10.0, "london"), + _tier_fact("region", "E12000001", 20.0, "north-east"), + _tier_fact("country", "W92000004", 30.0, "wales"), + _tier_fact("country", "K02000001", 60.0, "uk"), + ] + + +def test_author_target_references_fans_out_over_a_geography_roster() -> None: + config = TargetReferenceAuthoringConfig( + target_period=2025, + geography_fanout_by_target_id={"ons.age.0_10_by_region": _REGION_TIER_CELLS}, + geography_fanout_metadata=_tier_metadata, + source_fact_feed="synthetic", + ) + authored = author_target_references(_two_level_contract(), _tier_facts(), config) + names = [row["name"] for row in authored.references] + assert names == [ + "ons.age.0_10_by_region@E12000007", + "ons.age.0_10_by_region@E12000001", + "ons.age.0_10_by_region@W92000004", + ] + london = authored.references[0] + # Each cell is its own measure column, never the shared metric name. + assert london["measure"] == london["name"] + assert london["entity"] == "person" + assert london["ledger_selector"]["geography_level"] == "region" + assert london["ledger_selector"]["geography_id"] == "E12000007" + assert london["metadata"]["contract_target_id"] == "ons.age.0_10_by_region" + assert london["metadata"]["geography_level"] == "region" + assert london["metadata"]["geography_id"] == "E12000007" + assert json.loads(london["metadata"]["geography_predicate"]) == { + "variable": "region", + "operator": "==", + "value": "E12000007", + "map_to": "person", + } + assert london["metadata"]["cross_grain_grain"] == "region" + wales = authored.references[2] + assert wales["ledger_selector"]["geography_level"] == "country" + # The UK-wide fact is no cell of the roster: nothing binds it. + assert "K02000001" not in json.dumps(authored.references) + + report = authored.membership_report + assert report["active_reference_count"] == 3 + assert report["status_counts"] == {"active": 3} + assert report["geography_pins"]["ons.age.0_10_by_region"] == { + "geography_fanout": [ + {"geography_level": level, "geography_id": area_id} + for level, area_id in _REGION_TIER_CELLS + ] + } + candidates = report["targets"]["ons.age.0_10_by_region"]["candidates"] + assert [ + ( + entry["geography_level"], + entry["geography_id"], + entry["status"], + entry["resolved_value"], + ) + for entry in candidates + ] == [ + ("region", "E12000007", "active", 10.0), + ("region", "E12000001", "active", 20.0), + ("country", "W92000004", "active", 30.0), + ] + + +def test_geography_fanout_refuses_an_absent_roster_cell() -> None: + config = TargetReferenceAuthoringConfig( + target_period=2025, + geography_fanout_by_target_id={"ons.age.0_10_by_region": _REGION_TIER_CELLS}, + source_fact_feed="synthetic", + ) + facts = [fact for fact in _tier_facts() if fact["geography"]["id"] != "W92000004"] + with pytest.raises(ValueError, match="Unsigned geography fan-out absence") as info: + author_target_references(_two_level_contract(), facts, config) + assert "W92000004" in str(info.value) + + +def test_geography_fanout_refuses_a_pin_on_the_same_target() -> None: + config = TargetReferenceAuthoringConfig( + target_period=2025, + geography_pins={ + "ons.age.0_10_by_region": { + "geography_level": "country", + "geography_id": "K02000001", + } + }, + geography_fanout_by_target_id={"ons.age.0_10_by_region": _REGION_TIER_CELLS}, + source_fact_feed="synthetic", + ) + with pytest.raises(ValueError, match="both a geography pin"): + author_target_references(_two_level_contract(), _tier_facts(), config) + + +def test_geography_fanout_refuses_duplicate_cells_and_non_string_metadata() -> None: + duplicated = TargetReferenceAuthoringConfig( + target_period=2025, + geography_fanout_by_target_id={ + "ons.age.0_10_by_region": (("region", "E12000007"), ("region", "E12000007")) + }, + source_fact_feed="synthetic", + ) + with pytest.raises(ValueError, match="twice"): + author_target_references(_two_level_contract(), _tier_facts(), duplicated) + + non_string = TargetReferenceAuthoringConfig( + target_period=2025, + geography_fanout_by_target_id={"ons.age.0_10_by_region": _REGION_TIER_CELLS}, + geography_fanout_metadata=lambda *args: {"cross_grain_grain": 1}, + source_fact_feed="synthetic", + ) + with pytest.raises(ValueError, match="must be a string"): + author_target_references(_two_level_contract(), _tier_facts(), non_string) diff --git a/packages/microcosm-build/tests/test_uk_geography_ladder.py b/packages/microcosm-build/tests/test_uk_geography_ladder.py index 5131be258..679a349dd 100644 --- a/packages/microcosm-build/tests/test_uk_geography_ladder.py +++ b/packages/microcosm-build/tests/test_uk_geography_ladder.py @@ -280,9 +280,7 @@ def test_expected_support_matches_hand_computed_two_stage_result(tmp_path) -> No ), ) ) - household = pd.DataFrame( - {"household_id": [1, 2], "region": ["LONDON", "London"]} - ) + household = pd.DataFrame({"household_id": [1, 2], "region": ["LONDON", "London"]}) support = expected_uk_ladder_area_support(household, ladder, n_clones=2) @@ -664,3 +662,35 @@ def test_join_raises_on_unmatched_lad_itl() -> None: oa_ward=_oa_ward(), lad_itl=lad_itl, ) + + +def test_region_tier_roster_and_enum_table_are_consistent() -> None: + from microcosm.build.uk_runtime.frs_spine import REGION_MAP + from microcosm.build.uk_runtime.geography_ladder import ( + UK_ENGLAND_WALES_REGION_CODES, + UK_LADDER_NATION_REGION_CODES, + UK_REGION_TIER, + UK_REGION_TIER_ENUM, + ) + + codes = [code for _, code in UK_REGION_TIER] + assert len(codes) == 12 == len(set(codes)) + assert [code for level, code in UK_REGION_TIER if level == "region"] == [ + code for code in UK_ENGLAND_WALES_REGION_CODES if code.startswith("E12") + ] + assert [code for level, code in UK_REGION_TIER if level == "country"] == [ + "W92000004", + "S92000003", + "N92000002", + ] + # Every spine region enum has exactly one tier code, and every tier code + # names a spine region: the predicate a fan-out row carries can never name + # a region the frame does not carry. + assert set(UK_REGION_TIER_ENUM) == set(codes) + assert sorted(UK_REGION_TIER_ENUM.values()) == sorted(REGION_MAP.values()) + assert len(set(UK_REGION_TIER_ENUM.values())) == 12 + assert set(UK_LADDER_NATION_REGION_CODES.values()) == { + "W92000004", + "S92000003", + "N92000002", + } diff --git a/packages/microcosm-build/tests/test_uk_national_calibration.py b/packages/microcosm-build/tests/test_uk_national_calibration.py index 15c944399..d005926da 100644 --- a/packages/microcosm-build/tests/test_uk_national_calibration.py +++ b/packages/microcosm-build/tests/test_uk_national_calibration.py @@ -41,7 +41,7 @@ from microcosm.calibrate import TargetRegistry, TargetSpec from microcosm.frame import EntitySchema, Frame, WeightKind, Weights -ACTIVE_REFERENCE_COUNT = 424 +ACTIVE_REFERENCE_COUNT = 595 def _uc_reference(**overrides) -> LedgerTargetReference: diff --git a/packages/microcosm-build/tests/test_uk_target_references.py b/packages/microcosm-build/tests/test_uk_target_references.py index 4656704dd..4d3c01d79 100644 --- a/packages/microcosm-build/tests/test_uk_target_references.py +++ b/packages/microcosm-build/tests/test_uk_target_references.py @@ -34,6 +34,10 @@ TargetReferenceAuthoringConfig, author_target_references, ) +from microcosm.build.uk_runtime.geography_ladder import ( + UK_REGION_TIER, + UK_REGION_TIER_ENUM, +) from microcosm.build.uk_runtime.local_target_census import _LEDGER_FACT_FEED_PIN from microcosm.build.uk_runtime.national_chronicle_feed import ( load_uk_national_chronicle_feed, @@ -56,7 +60,8 @@ _value_operation_by_target_id, ) -ACTIVE_REFERENCE_COUNT = 424 +ACTIVE_REFERENCE_COUNT = 595 +REGION_TIER_LEVEL = {code: level for level, code in UK_REGION_TIER} UK_DATA_REPO = "policyengine-" + "uk-data" @@ -233,21 +238,34 @@ def test_uk_target_references_follow_contract_derivation_rules() -> None: contract_target_id = reference["metadata"]["contract_target_id"] target = targets_by_id[contract_target_id] binding = target["bindings"]["policyengine"] + fanout_cell = reference["metadata"].get("geography_id") for key, value in target["ledger_selector"].items(): if key == "dimension_values": assert value.items() <= reference["ledger_selector"][key].items() continue assert reference["ledger_selector"][key] == value - # Region-only contract targets (the DfT London bus rows) pin the - # publisher's region-stamped fact; everything else pins a country. - expected_level = ( - "region" - if list(target.get("geography_levels") or ()) == ["region"] - else "country" - ) - assert reference["ledger_selector"]["geography_level"] == expected_level - assert reference["ledger_selector"]["geography_id"] + if fanout_cell: + # Two-level (country + region) targets fan out over the region + # tier: each cell pins the tier's level for its area and is named + # and measured target_id@geography_id (microcosm#905). + assert reference["name"] == f"{contract_target_id}@{fanout_cell}" + assert sorted(target["geography_levels"]) == ["country", "region"] + assert ( + reference["ledger_selector"]["geography_level"] + == (REGION_TIER_LEVEL[fanout_cell]) + ) + assert reference["ledger_selector"]["geography_id"] == fanout_cell + else: + # Region-only contract targets (the DfT London bus rows) pin the + # publisher's region-stamped fact; everything else pins a country. + expected_level = ( + "region" + if list(target.get("geography_levels") or ()) == ["region"] + else "country" + ) + assert reference["ledger_selector"]["geography_level"] == expected_level + assert reference["ledger_selector"]["geography_id"] assert reference["entity"] == _expected_reference_entity(target) expected_measure = ( binding["metric_name"] @@ -273,6 +291,26 @@ def test_uk_target_references_follow_contract_derivation_rules() -> None: expected_metadata["measurement_period"] = str(binding["measurement_period"]) if binding.get("require_matching_fact_period"): expected_metadata["source_period_policy"] = "exact_observation" + if fanout_cell: + expected_metadata.update( + { + "geography_level": REGION_TIER_LEVEL[fanout_cell], + "geography_id": fanout_cell, + "geography_predicate": json.dumps( + { + "entity": "household", + "variable": "region", + "operator": "==", + "value": UK_REGION_TIER_ENUM[fanout_cell], + "reduce": "any", + "map_to": _expected_reference_entity(target), + }, + sort_keys=True, + separators=(",", ":"), + ), + "cross_grain_grain": "region", + } + ) assert reference["metadata"] == expected_metadata # The measure is a prepared column, so the pointed-to contract binding # must carry what the microcosm#622 materializer needs to prepare it. @@ -595,7 +633,7 @@ def test_uk_target_reference_membership_report_is_packaged() -> None: assert membership["target_period"] == 2025 assert membership["active_reference_count"] == ACTIVE_REFERENCE_COUNT assert membership["status_counts"] == { - "active": 424, + "active": 595, "no_fact_at_or_before_period": 7, "signed_excluded": 7, } @@ -634,12 +672,30 @@ def test_uk_target_reference_membership_report_is_packaged() -> None: { "family": "council_tax_stock", "status": "active_declared_rows", - "active_reference_count": 18, + "active_reference_count": 90, + "signed_rationale": ( + "VOA (England) and Scottish Government CTAXBASE (Scotland) " + "council-tax stock bands are declared as nine explicit target " + "rows each, including total. The Scottish rows resolve with " + "their country-level geography and band pin; the English rows " + "fan out over the nine English regions of the region tier " + "(microcosm#905), each cell resolving VOA's region-stamped " + "band count with the same band pin." + ), + }, + { + "family": "ons_population", + "status": "active_region_tier_fanout", + "active_reference_count": 108, "signed_rationale": ( - "VOA (England and Wales) and Scottish Government CTAXBASE " - "(Scotland) council-tax stock bands are declared as nine " - "explicit target rows each, including total, and each resolves " - "with its country-level geography and band pin." + "The nine ONS population-by-age-band targets fan out over the " + "twelve-area region tier (nine English regions at Chronicle's " + "region level, Wales, Scotland and Northern Ireland at country " + "level), one reference per area, each scoped on the spine by " + "a household-region predicate and placed at the region grain " + "of the cross-grain rule (microcosm#905). The former single " + "UK-wide row per band is retired: the tier sums to it within " + "the same publication." ), }, ] @@ -1216,3 +1272,52 @@ def test_public_cgt_parity_fixture_rejects_stale_values_and_unsigned_names(mutat signed = [r for r in signed if r["name"] != omitted] gate = ledger_compile_parity_gate(actual, fixture, signed_differences=signed) assert not gate.passed + + +def test_two_level_targets_fan_out_over_the_region_tier() -> None: + resource = _load_uk_resource("target_references.json") + membership = _load_uk_resource("target_reference_membership.json") + contract = _load_uk_resource("uk_population_targets.json") + two_level = [ + target["target_id"] + for target in contract["targets"] + if sorted(target.get("geography_levels") or ()) == ["country", "region"] + ] + assert len(two_level) == 18 + ons = [target_id for target_id in two_level if target_id.startswith("ons.")] + voa = [target_id for target_id in two_level if target_id.startswith("voa.")] + assert len(ons) == 9 and len(voa) == 9 + by_contract: dict[str, list[dict]] = {} + for reference in resource["target_references"]: + by_contract.setdefault(reference["metadata"]["contract_target_id"], []).append( + reference + ) + tier_codes = [code for _, code in UK_REGION_TIER] + english = [code for code in tier_codes if code.startswith("E12")] + for target_id in two_level: + rows = by_contract[target_id] + cells = [row["metadata"]["geography_id"] for row in rows] + # The retired single country row is gone: every row is a tier cell. + assert all("@" in row["name"] for row in rows), target_id + assert cells == (tier_codes if target_id in ons else english), target_id + assert [row["measure"] for row in rows] == [row["name"] for row in rows] + assert {row["metadata"]["cross_grain_grain"] for row in rows} == {"region"} + for row in rows: + predicate = json.loads(row["metadata"]["geography_predicate"]) + assert ( + predicate["value"] + == UK_REGION_TIER_ENUM[row["metadata"]["geography_id"]] + ) + assert predicate["map_to"] == row["entity"] + pins = membership["geography_pins"][target_id] + assert [cell["geography_id"] for cell in pins["geography_fanout"]] == cells + candidates = membership["targets"][target_id]["candidates"] + assert [entry["geography_id"] for entry in candidates] == cells + assert {entry["status"] for entry in candidates} == {"active"} + assert sum(len(by_contract[target_id]) for target_id in two_level) == 189 + # The twelve ONS cells of a band sum to the retired UK row of the same + # publication (the 0-9 band: 7,553,013 at mid-2024). + zero_to_nine = membership["targets"]["ons.population.age_0_9_by_region"] + assert sum(entry["resolved_value"] for entry in zero_to_nine["candidates"]) == ( + 7_553_013.0 + ) diff --git a/tools/generate_uk_target_references.py b/tools/generate_uk_target_references.py index f2908e80d..712fb9628 100644 --- a/tools/generate_uk_target_references.py +++ b/tools/generate_uk_target_references.py @@ -21,6 +21,10 @@ author_target_references, target_references_resource, ) +from microcosm.build.uk_runtime.geography_ladder import ( + UK_REGION_TIER, + UK_REGION_TIER_ENUM, +) from microcosm.build.uk_runtime.uc_source_periods import uc_source_month_metadata UK_GEOGRAPHY_IDS = { @@ -105,6 +109,8 @@ def main() -> None: config = TargetReferenceAuthoringConfig( target_period=args.period, geography_pins=_geography_pins(contract), + geography_fanout_by_target_id=_geography_fanout(contract), + geography_fanout_metadata=_geography_fanout_metadata, fanout_name=lambda target, fact: _fanout_name( target, fact, @@ -193,17 +199,103 @@ def _geography_pins(contract: Mapping[str, Any]) -> dict[str, dict[str, str]]: exists. Every other national target pins at country level. """ + fanned_out = _geography_fanout(contract) pins: dict[str, dict[str, str]] = {} for target in contract.get("targets", ()): + target_id = str(target["target_id"]) + if target_id in fanned_out: + # A two-level target takes its geographies from the region tier + # roster (microcosm#905); a single pin would collapse it back to + # the country row the roster replaces. + continue levels = tuple(str(level) for level in target.get("geography_levels") or ()) level = "region" if levels == ("region",) else "country" - pins[str(target["target_id"])] = { + pins[target_id] = { "geography_level": level, "geography_id": _geography_id_for_target(target), } return pins +#: The declared level set that fans out over the region tier instead of +#: pinning one country: a target that is published both nationally and by +#: region (ONS mid-year population by age band, VOA council-tax stock by band). +REGION_TIER_FANOUT_LEVELS = frozenset({"country", "region"}) + + +def _geography_fanout( + contract: Mapping[str, Any], +) -> dict[str, tuple[tuple[str, str], ...]]: + """Region-tier cells for every two-level (country + region) target. + + The nine English regions are always in; the three nations join only when + the binding does not already pin a country itself (the VOA stock bindings + filter ``country == ENGLAND`` because the Welsh, Scottish and Northern + Irish stocks are separate publications and contract families), so a + nation cell is never a row the binding would measure as zero. + """ + + fanout: dict[str, tuple[tuple[str, str], ...]] = {} + for target in contract.get("targets", ()): + levels = {str(level) for level in target.get("geography_levels") or ()} + if levels != REGION_TIER_FANOUT_LEVELS: + continue + cells = [(level, code) for level, code in UK_REGION_TIER if level == "region"] + if not _binding_pins_country(target["bindings"]["policyengine"]): + cells.extend( + (level, code) for level, code in UK_REGION_TIER if level == "country" + ) + fanout[str(target["target_id"])] = tuple(cells) + return fanout + + +def _binding_pins_country(binding: Mapping[str, Any]) -> bool: + predicates = [ + *binding.get("filters", ()), + *binding.get("household_conditions", ()), + ] + return any( + str(predicate.get("variable") or predicate.get("concept") or "") == "country" + for predicate in predicates + if isinstance(predicate, Mapping) + ) + + +def _geography_fanout_metadata( + target: Mapping[str, Any], + geography_level: str, + geography_id: str, + entity: str, +) -> dict[str, str]: + """Scope one region-tier row to its area on the spine. + + The predicate compares the household's ``region`` enum (FRS ``gvtregno`` + through ``REGION_MAP``) with the tier code's enum name and projects the + household match to the binding's own entity, the way the incumbent's + ``compute_regional_age`` masks persons by their household's region. The + ``cross_grain_grain`` stamp places every tier row, the three nation rows + included, at the ``region`` grain of the cross-grain rule; Chronicle's + ``country`` stamp on those three stays on the selector and the ledger + metadata untouched. + """ + + del target, geography_level + predicate = { + "entity": "household", + "variable": "region", + "operator": "==", + "value": UK_REGION_TIER_ENUM[geography_id], + "reduce": "any", + "map_to": entity, + } + return { + "geography_predicate": json.dumps( + predicate, sort_keys=True, separators=(",", ":") + ), + "cross_grain_grain": "region", + } + + SCOTGOV_COUNCIL_TAX_STOCK_PREFIX = "scotgov.council_tax_stock." # Target-id prefixes whose geography pins cannot come from the nation-substring @@ -504,10 +596,32 @@ def _add_uk_membership_accounting( "status": "active_declared_rows", "active_reference_count": council_tax_count, "signed_rationale": ( - "VOA (England and Wales) and Scottish Government CTAXBASE " - "(Scotland) council-tax stock bands are declared as nine " - "explicit target rows each, including total, and each resolves " - "with its country-level geography and band pin." + "VOA (England) and Scottish Government CTAXBASE (Scotland) " + "council-tax stock bands are declared as nine explicit target " + "rows each, including total. The Scottish rows resolve with " + "their country-level geography and band pin; the English rows " + "fan out over the nine English regions of the region tier " + "(microcosm#905), each cell resolving VOA's region-stamped " + "band count with the same band pin." + ), + }, + { + "family": "ons_population", + "status": "active_region_tier_fanout", + "active_reference_count": sum( + 1 + for reference in references + if reference["metadata"]["contract_target_id"].endswith("_by_region") + ), + "signed_rationale": ( + "The nine ONS population-by-age-band targets fan out over the " + "twelve-area region tier (nine English regions at Chronicle's " + "region level, Wales, Scotland and Northern Ireland at country " + "level), one reference per area, each scoped on the spine by " + "a household-region predicate and placed at the region grain " + "of the cross-grain rule (microcosm#905). The former single " + "UK-wide row per band is retired: the tier sums to it within " + "the same publication." ), }, ] From 74226c3fe703e1df67f1d7992451a6f1e401dbfe Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mar=C3=ADa=20Juaristi?= <127882282+juaristi22@users.noreply.github.com> Date: Fri, 11 Sep 2026 16:45:03 +0200 Subject: [PATCH 08/10] Scope region-tier cells with a per-reference geography predicate (#905) A fanned-out reference shares its contract binding with every sibling cell; what distinguishes the cells is the predicate the authoring stamps into metadata.geography_predicate (a JSON object in the ordinary predicate vocabulary). The shared materializer appends it to the binding's filters before masking; references without the key are untouched, so no existing country-level reference is re-scoped by its geography stamp, and a provider binding refuses it because providers do not read filters. The UK cells carry a household-region predicate (entity household, variable region, reduce any, map_to the binding entity), resolved through the adapter's existing household_condition projection: persons for the ONS bands, households for the VOA bands. No adapter change. Tests cover the shared mechanism on the stub adapter and the UK frame adapter at both grains (81 VOA region cells, 12 ONS cells). Co-Authored-By: Claude Fable 5.1 --- .../microcosm/build/target_materialization.py | 43 +++++++ .../tests/test_target_materialization.py | 105 +++++++++++++++++ .../tests/test_uk_council_tax_targets.py | 111 +++++++++++++++--- 3 files changed, 241 insertions(+), 18 deletions(-) diff --git a/packages/microcosm-build/src/microcosm/build/target_materialization.py b/packages/microcosm-build/src/microcosm/build/target_materialization.py index 2629e4507..c2b84afed 100644 --- a/packages/microcosm-build/src/microcosm/build/target_materialization.py +++ b/packages/microcosm-build/src/microcosm/build/target_materialization.py @@ -2,6 +2,7 @@ from __future__ import annotations +import json import math import re from collections.abc import Callable, Mapping, Sequence @@ -490,6 +491,7 @@ def materialize_target_bindings( contract_target_id = spec.metadata.get("contract_target_id") target = contract_targets.get(str(contract_target_id)) binding = target["bindings"]["policyengine"] if target is not None else {} + binding = _with_geography_predicate(binding, spec) if ( hasattr(adapter, "has_column") and adapter.has_column(spec.entity, spec.measure) @@ -629,6 +631,47 @@ def input_substitution_counterfactual( raise ValueError("adapter does not provide counterfactual_delta") +def _with_geography_predicate( + binding: Mapping[str, Any], + spec: Any, +) -> Mapping[str, Any]: + """Scope a contract binding to one reference's declared geography. + + A reference authored by a geography fan-out shares its contract binding + with every sibling cell; what distinguishes the cells is the predicate the + authoring stamped into ``metadata.geography_predicate`` (a JSON object in + the ordinary predicate vocabulary). It is appended to the binding's + ``filters`` verbatim. References without the key are untouched, so no + existing country-level reference is re-scoped by its geography stamp. + """ + + metadata = getattr(spec, "metadata", None) or {} + raw = metadata.get("geography_predicate") + if not raw: + return binding + name = getattr(spec, "name", "") + try: + predicate = json.loads(str(raw)) + except json.JSONDecodeError as error: + raise ValueError( + f"{name!r}: metadata.geography_predicate is not valid JSON: {error}" + ) from None + if not isinstance(predicate, Mapping) or not predicate.get("variable"): + raise ValueError( + f"{name!r}: metadata.geography_predicate must be a predicate object " + "naming a variable." + ) + if binding.get("kind"): + raise ValueError( + f"{name!r}: a geography predicate cannot scope a provider binding " + f"of kind {binding['kind']!r}; providers do not read filters." + ) + return { + **binding, + "filters": [*binding.get("filters", ()), dict(predicate)], + } + + def _prepared_column_values( adapter: Any, entity: str, diff --git a/packages/microcosm-build/tests/test_target_materialization.py b/packages/microcosm-build/tests/test_target_materialization.py index 4c81e6173..88523e546 100644 --- a/packages/microcosm-build/tests/test_target_materialization.py +++ b/packages/microcosm-build/tests/test_target_materialization.py @@ -1,3 +1,4 @@ +import json import numpy as np import pandas as pd import pytest @@ -1071,3 +1072,107 @@ def test_declared_band_filter_dimension_breaks_the_tie(): assert result.skipped == () assert list(adapter.tables["person"]["income_band_0"]) == [1.0, 0.0, 0.0] assert list(adapter.tables["person"]["income_band_20"]) == [0.0, 1.0, 1.0] + + +def _geography_predicate_registry(predicate: str | None) -> TargetRegistry: + metadata = {"contract_target_id": "adult_income"} + if predicate is not None: + metadata["geography_predicate"] = predicate + return TargetRegistry( + [ + TargetSpec( + name="adult_income@LONDON", + entity="person", + measure="adult_income@LONDON", + value=50.0, + source="test", + metadata=metadata, + ) + ], + country="uk", + ) + + +_ADULT_INCOME_CONTRACT = { + "adult_income": { + "bindings": { + "policyengine": { + "value_variable": "income", + "filters": [{"variable": "age", "operator": ">=", "value": 18}], + } + } + } +} + + +def test_geography_predicate_scopes_a_shared_binding_to_one_reference(): + adapter = StubAdapter() + adapter.tables["person"]["region"] = np.array(["LONDON", "LONDON", "WALES"]) + registry = _geography_predicate_registry( + json.dumps({"variable": "region", "operator": "==", "value": "LONDON"}) + ) + result = materialize_target_bindings( + adapter, registry, _ADULT_INCOME_CONTRACT, period=2025 + ) + assert result.skipped == () + # The contract's age filter still applies; the predicate removes the + # Welsh adult, and nothing in the contract itself changed. + assert adapter.tables["person"]["adult_income@LONDON"].tolist() == [ + 0.0, + 20.0, + 0.0, + ] + assert _ADULT_INCOME_CONTRACT["adult_income"]["bindings"]["policyengine"][ + "filters" + ] == [{"variable": "age", "operator": ">=", "value": 18}] + + +def test_reference_without_geography_predicate_is_untouched(): + adapter = StubAdapter() + registry = _geography_predicate_registry(None) + result = materialize_target_bindings( + adapter, registry, _ADULT_INCOME_CONTRACT, period=2025 + ) + assert result.skipped == () + assert adapter.tables["person"]["adult_income@LONDON"].tolist() == [ + 0.0, + 20.0, + 30.0, + ] + + +@pytest.mark.parametrize( + "predicate, message", + [ + ("{not json", "not valid JSON"), + (json.dumps(["region"]), "must be a predicate object"), + (json.dumps({"operator": "==", "value": "LONDON"}), "naming a variable"), + ], +) +def test_malformed_geography_predicate_refuses_materialization(predicate, message): + adapter = StubAdapter() + registry = _geography_predicate_registry(predicate) + with pytest.raises(ValueError, match=message): + materialize_target_bindings( + adapter, registry, _ADULT_INCOME_CONTRACT, period=2025 + ) + + +def test_geography_predicate_refuses_provider_bindings(): + adapter = StubAdapter() + registry = _geography_predicate_registry( + json.dumps({"variable": "region", "operator": "==", "value": "LONDON"}) + ) + contract = { + "adult_income": { + "bindings": { + "policyengine": { + "kind": "input_substitution_counterfactual", + "zeroed_input": "salary_sacrifice", + "output_variable": "baseline_tax", + } + } + } + } + with pytest.raises(ValueError, match="cannot scope a provider binding"): + materialize_target_bindings(adapter, registry, contract, period=2025) diff --git a/packages/microcosm-build/tests/test_uk_council_tax_targets.py b/packages/microcosm-build/tests/test_uk_council_tax_targets.py index e150eec93..dd8c8d371 100644 --- a/packages/microcosm-build/tests/test_uk_council_tax_targets.py +++ b/packages/microcosm-build/tests/test_uk_council_tax_targets.py @@ -9,6 +9,7 @@ import pytest from microcosm.build.country_spec import load_country_spec +from microcosm.build.uk_runtime.geography_ladder import UK_REGION_TIER_ENUM from microcosm.build.uk_runtime.ledger_targets import ( UKFrameTargetAdapter, materialize_uk_ledger_targets, @@ -36,15 +37,9 @@ def _membership() -> dict: ) -def test_national_voa_matrix_counts_only_england_for_every_band_and_total() -> None: - """The E92000001 fact must not absorb Wales/Scotland/NI household mass.""" - rows = [ - (country, band) - for country in ("ENGLAND", "WALES", "SCOTLAND", "NORTHERN_IRELAND") - for band in "ABCDEFGH" - ] +def _region_frame(rows: list[tuple[str, str, str]]) -> Frame: ids = np.arange(len(rows), dtype="int64") - frame = Frame( + return Frame( { "person": pd.DataFrame( {"person_id": ids, "person_benunit_id": ids, "person_household_id": ids} @@ -53,8 +48,9 @@ def test_national_voa_matrix_counts_only_england_for_every_band_and_total() -> N "household": pd.DataFrame( { "household_id": ids, - "country": [x[0] for x in rows], - "council_tax_band": [x[1] for x in rows], + "country": [row[0] for row in rows], + "region": [row[1] for row in rows], + "council_tax_band": [row[2] for row in rows], "household_num_benunits": np.ones(len(rows)), } ), @@ -62,13 +58,14 @@ def test_national_voa_matrix_counts_only_england_for_every_band_and_total() -> N EntitySchema(group_entities=("benunit", "household")), {"household": Weights(np.ones(len(rows)), WeightKind.DESIGN)}, ) + + +def _registry_for(prefix: str) -> tuple[list, TargetRegistry]: refs = [ r for r in load_country_spec("uk").target_references - if r.name.startswith("voa.council_tax_stock.") + if r.name.startswith(prefix) ] - assert len(refs) == 9 - assert all(r.ledger_selector["geography_id"] == "E92000001" for r in refs) registry = TargetRegistry( [ TargetSpec( @@ -84,7 +81,33 @@ def test_national_voa_matrix_counts_only_england_for_every_band_and_total() -> N ], country="uk", ) - adapter = UKFrameTargetAdapter(frame) + return refs, registry + + +def test_national_voa_matrix_counts_only_its_region_for_every_band_and_total() -> None: + """Each region cell must count only that region's English band households. + + The English VOA stock fans out over the nine English regions + (microcosm#905); a Welsh, Scottish or Northern Irish household never + enters any cell, and a Londoner never enters the North East's. + """ + + areas = ( + ("ENGLAND", "NORTH_EAST"), + ("ENGLAND", "LONDON"), + ("ENGLAND", "SOUTH_EAST"), + ("WALES", "WALES"), + ("SCOTLAND", "SCOTLAND"), + ("NORTHERN_IRELAND", "NORTHERN_IRELAND"), + ) + rows = [(country, region, band) for country, region in areas for band in "ABCDEFGH"] + refs, registry = _registry_for("voa.council_tax_stock.") + assert len(refs) == 81 + assert {r.ledger_selector["geography_level"] for r in refs} == {"region"} + assert sorted({r.ledger_selector["geography_id"] for r in refs}) == [ + f"E1200000{index}" for index in range(1, 10) + ] + adapter = UKFrameTargetAdapter(_region_frame(rows)) result = materialize_uk_ledger_targets( adapter, registry, period=2025, band_edge_registry=registry ) @@ -93,17 +116,69 @@ def test_national_voa_matrix_counts_only_england_for_every_band_and_total() -> N adapter.to_frame(), registry.to_target_set(), weight_entity="household" ) for name, values in zip(problem.names, problem.matrix.toarray(), strict=True): + target_id, code = name.split("@")[:2] band = ( - name.split("@")[0].rsplit("band_", 1)[-1].upper() - if "band_" in name - else None + target_id.rsplit("band_", 1)[-1].upper() if "band_" in target_id else None ) + region = UK_REGION_TIER_ENUM[code] expected = [ - country == "ENGLAND" and (band is None or b == band) for country, b in rows + country == "ENGLAND" and r == region and (band is None or b == band) + for country, r, b in rows ] np.testing.assert_array_equal(values, expected, err_msg=name) +def test_national_ons_region_cells_count_only_their_region_at_person_grain() -> None: + """The region predicate is a household fact projected to each person.""" + + household_ids = np.arange(3, dtype="int64") + person_ids = np.arange(4, dtype="int64") + frame = Frame( + { + "person": pd.DataFrame( + { + "person_id": person_ids, + "person_benunit_id": np.array([0, 0, 1, 2], dtype="int64"), + "person_household_id": np.array([0, 0, 1, 2], dtype="int64"), + "age": np.array([5.0, 40.0, 7.0, 3.0]), + } + ), + "benunit": pd.DataFrame({"benunit_id": household_ids}), + "household": pd.DataFrame( + { + "household_id": household_ids, + "region": ["LONDON", "WALES", "SCOTLAND"], + "household_num_benunits": np.ones(3), + } + ), + }, + EntitySchema(group_entities=("benunit", "household")), + {"household": Weights(np.ones(3), WeightKind.DESIGN)}, + ) + refs, registry = _registry_for("ons.population.age_0_9_by_region@") + assert len(refs) == 12 + adapter = UKFrameTargetAdapter(frame) + result = materialize_uk_ledger_targets( + adapter, registry, period=2025, band_edge_registry=registry + ) + assert not result.skipped + problem = build_constraint_matrix( + adapter.to_frame(), registry.to_target_set(), weight_entity="household" + ) + # Matrix names carry the target period as a third "@" segment. + by_name = { + "@".join(name.split("@")[:2]): values + for name, values in zip(problem.names, problem.matrix.toarray(), strict=True) + } + prefix = "ons.population.age_0_9_by_region@" + # London: one child under ten; the 40-year-old is outside the band. + assert by_name[f"{prefix}E12000007"].tolist() == [1.0, 0.0, 0.0] + assert by_name[f"{prefix}W92000004"].tolist() == [0.0, 1.0, 0.0] + assert by_name[f"{prefix}S92000003"].tolist() == [0.0, 0.0, 1.0] + assert by_name[f"{prefix}N92000002"].tolist() == [0.0, 0.0, 0.0] + assert by_name[f"{prefix}E12000001"].tolist() == [0.0, 0.0, 0.0] + + def test_council_tax_band_cells_activate_and_defer_as_measured() -> None: membership = _membership() expected_active = { From 322fc440913da411a0d198b99cef19b12267c186 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mar=C3=ADa=20Juaristi?= <127882282+juaristi22@users.noreply.github.com> Date: Fri, 11 Sep 2026 16:45:03 +0200 Subject: [PATCH 09/10] Make region a cross-grain tier with tier-code legs (#905) UK_CROSS_GRAIN_RULE's precedence becomes country > region > constituency > la. Every region-tier code is its own leg (UK_CROSS_GRAIN_PARENT_ GEOGRAPHY_LEGS; the K02/K03/E92 parents list the tier members they contain), so a region cell is the control for the constituencies and authorities inside it. English areas resolve their leg through the crosswalk's new region_code_by_area, derived per level from the sha-pinned OA ladder (every constituency and authority carries exactly one tier code; London holds 75 constituencies and 33 authorities); Welsh, Scottish and Northern Irish areas resolve by GSS prefix, and an English code outside the crosswalk refuses instead of falling back to an England leg no control covers. Leg licences follow the same function. Chronicle stamps the Welsh, Scottish and Northern Irish population at country; on the surface those rows are the same ITL1 tier as the nine English regions and must not outrank them, so uk_local_target_surface reads the cross_grain_grain stamp the generator writes and places them at the region grain. The selector and ledger metadata keep Chronicle's level. The shared operator now treats any winning row whose geography declares legs as a declared parent, whatever grain it sits at, so region controls get the same dedupe, bridge-completeness check and receipt naming as country controls. Compile parity: align_uk_national_registry_parity_fixture renames the incumbent's ons/_age_* and voa/council_tax// rows to the tier names (applied by the receipt tool and the battery gate loader), so the 108 region x age rows and 81 regional VOA rows compare as matched. 2025 receipts: fixture_only 269 -> 80, ledger_only 56 -> 38 (the retired country rows), calibration_drift 172 -> 280 (the ONS cells drift against the incumbent's rounded subnational projections, with a ruled rationale on each row; every VOA cell matches exactly). The data-target parity concern cross_grain_council_tax_stock_england_region is ported. Co-Authored-By: Claude Fable 5.1 --- docs/uk-chronicle-feed-repin.md | 7 + .../src/microcosm/build/cross_grain.py | 10 +- ...ity_incumbent_2025_signed_differences.json | 3069 +++++++---------- ...ty_production_2023_signed_differences.json | 2377 ++++++------- .../build/uk/local_area_crosswalk.json | 1019 +++++- .../build/uk/uk_data_target_parity.json | 13 +- .../build/uk_runtime/battery_bindings.py | 5 +- .../build/uk_runtime/data_target_parity.py | 13 +- .../build/uk_runtime/ledger_targets.py | 198 +- .../tests/test_uk_ledger_targets.py | 345 +- .../tests/test_uk_local_area_crosswalk.py | 26 + ...edger_compile_parity_signed_differences.py | 17 +- tools/generate_uk_local_area_crosswalk.py | 46 +- 13 files changed, 4043 insertions(+), 3102 deletions(-) diff --git a/docs/uk-chronicle-feed-repin.md b/docs/uk-chronicle-feed-repin.md index 40749f9a3..0e5aec61c 100644 --- a/docs/uk-chronicle-feed-repin.md +++ b/docs/uk-chronicle-feed-repin.md @@ -22,6 +22,13 @@ schema version. For a national update, update membership with `tools/generate_uk_target_references.py`. Verify the complete compiled target diff, including targets outside the intended policy area. The hermetic national regeneration test accepts `CHRONICLE_UK_FACTS`. +Two-level (country + region) contract targets fan out over the region tier +(`UK_REGION_TIER` in `uk_runtime/geography_ladder.py`), one reference per area +(microcosm#905); their cells resolve Chronicle's region- and country-stamped +facts, so a re-pin must carry all twelve areas or the generator refuses. +The cross-grain legs of English constituencies and authorities come from +`region_code_by_area` in `local_area_crosswalk.json`, regenerated from the +sha-pinned ladder with `tools/generate_uk_local_area_crosswalk.py`. For a separately reviewed local update, update `_LEDGER_FACT_FEED_PIN` in `uk_runtime/local_target_census.py`, the local validation-level pin, and their diff --git a/packages/microcosm-build/src/microcosm/build/cross_grain.py b/packages/microcosm-build/src/microcosm/build/cross_grain.py index 5e11cd908..23dde3eb4 100644 --- a/packages/microcosm-build/src/microcosm/build/cross_grain.py +++ b/packages/microcosm-build/src/microcosm/build/cross_grain.py @@ -770,7 +770,15 @@ def _winning_controls( } for position in group.winning_positions ] - if group.winning_grain == rule.grain_precedence[0]: + # A winning row whose geography declares its legs is a parent control in + # its own right, whatever grain it sits at: the top grain always is, and a + # middle tier (a region between country and constituency) is when the + # country declares a leg map for every one of its codes. Rows with no + # declared legs stay on the area-derived path below. + declared_parents = group.winning_grain == rule.grain_precedence[0] or all( + row["geography_id"] in rule.parent_geography_legs for row in rows + ) + if declared_parents: by_geography: dict[str, list[dict[str, Any]]] = {} for row in rows: by_geography.setdefault(row["geography_id"], []).append(row) diff --git a/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_incumbent_2025_signed_differences.json b/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_incumbent_2025_signed_differences.json index 266553cfd..2cb240d3e 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_incumbent_2025_signed_differences.json +++ b/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_incumbent_2025_signed_differences.json @@ -1,11 +1,11 @@ { - "compiled_count": 424, + "compiled_count": 595, "counts_by_kind": { - "calibration_drift": 172, - "fixture_only": 269, - "ledger_only": 56 + "calibration_drift": 280, + "fixture_only": 80, + "ledger_only": 38 }, - "difference_count": 497, + "difference_count": 398, "differences": [ { "fixture_value": 1608000.0, @@ -372,2899 +372,2314 @@ "reason": "Fixture row has no Ledger-compiled counterpart at this comparison period." }, { - "fixture_value": 726000.0, + "fixture_value": 304799621177.5439, "kind": "fixture_only", - "name": "ons/east_age_0_9", + "name": "ons/household_land_value/EAST_MIDLANDS", "period": 2025, "reason": "Fixture row has no Ledger-compiled counterpart at this comparison period." }, { - "fixture_value": 741000.0, + "fixture_value": 542672059385.554, "kind": "fixture_only", - "name": "ons/east_age_10_19", + "name": "ons/household_land_value/EAST_OF_ENGLAND", "period": 2025, "reason": "Fixture row has no Ledger-compiled counterpart at this comparison period." }, { - "fixture_value": 749000.0, + "fixture_value": 1188167933642.289, "kind": "fixture_only", - "name": "ons/east_age_20_29", + "name": "ons/household_land_value/LONDON", "period": 2025, "reason": "Fixture row has no Ledger-compiled counterpart at this comparison period." }, { - 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This compares that declaration with the frozen 2025 incumbent baseline; it does not redatum the observations to the comparison year or create a new year ruling." + "reason": "Ledger-compiled value differs from the fixture value at this comparison period." }, { - "fixture_value": 1222944.0, + "fixture_value": 144036.0, "kind": "calibration_drift", - "ledger_value": 1190123.8333333333, - "name": "dwp.uc.households_children_1", + "ledger_value": 149043.77777777778, + "name": "dwp/uc_payment_dist/SINGLE_annual_payment_7_200_to_8_400", "period": 2025, - "reason": "Inherited PR #891 contract with #892 source-window averaging: current paid-UC counts use the explicit January-December 2025 source windows on the ec7169 national feed. This compares that declaration with the frozen 2025 incumbent baseline; it does not redatum the observations to the comparison year or create a new year ruling." + "reason": "Ledger-compiled value differs from the fixture value at this comparison period." }, { - "fixture_value": 1058967.0, + "fixture_value": 377221.0, "kind": "calibration_drift", - "ledger_value": 1049895.5, - "name": "dwp.uc.households_children_2", + "ledger_value": 399750.22222222225, + "name": "dwp/uc_payment_dist/SINGLE_annual_payment_8_400_to_9_600", "period": 2025, - "reason": "Inherited PR #891 contract with #892 source-window averaging: current paid-UC counts use the explicit January-December 2025 source windows on the ec7169 national feed. 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This compares that declaration with the frozen 2025 incumbent baseline; it does not redatum the observations to the comparison year or create a new year ruling." + "reason": "Ledger-compiled value differs from the fixture value at this comparison period." }, { - "fixture_value": 166790.0, + "fixture_value": 67727478991.59665, "kind": "calibration_drift", - "ledger_value": 170713.66666666666, - "name": "dwp.uc.households_children_4", + "ledger_value": 119258000000.0, + "name": "hmrc.cgt.gains_total", "period": 2025, - "reason": "Inherited PR #891 contract with #892 source-window averaging: current paid-UC counts use the explicit January-December 2025 source windows on the ec7169 national feed. This compares that declaration with the frozen 2025 incumbent baseline; it does not redatum the observations to the comparison year or create a new year ruling." + "reason": "PR #889 selects HMRC FY2024-25 individuals-only observed gains of GBP 119,258,000,000, measured in 2024 at calibration index 2025. The frozen incumbent fixture retains its historical forecast/uprated GBP 67,727,478,991.60 value. This explicit observation-year and population-scope change does not rewrite the historical fixture." }, { - "fixture_value": 75910.0, + "fixture_value": 378000.0, "kind": "calibration_drift", - "ledger_value": 76320.08333333333, - "name": "dwp.uc.households_children_5_or_more", + "ledger_value": 551000.0, + "name": "hmrc.cgt.taxpayers_total", "period": 2025, - "reason": "Inherited PR #891 contract with #892 source-window averaging: current paid-UC counts use the explicit January-December 2025 source windows on the ec7169 national feed. This compares that declaration with the frozen 2025 incumbent baseline; it does not redatum the observations to the comparison year or create a new year ruling." + "reason": "PR #889 selects the HMRC FY2024-25 individuals-only observation of 551,000 taxpayers, measured in 2024 at calibration index 2025. The frozen incumbent fixture retains its historical 378,000 count." }, { - "fixture_value": 231368.0, + "fixture_value": 1236000000.0, "kind": "calibration_drift", - "ledger_value": 241130.91666666666, - "name": "dwp.uc.households_couple_no_children", + "ledger_value": 1000000000.0, + "name": "hmrc.salary_sacrifice.nics_relief_employee", "period": 2025, - "reason": "Inherited PR #891 contract: current paid-UC counts use the explicit January-December 2025 source windows on the ec7169 national feed. This compares that declaration with the frozen 2025 incumbent baseline; it does not redatum the observations to the comparison year or create a new year ruling." + "reason": "Ledger-compiled value differs from the fixture value at this comparison period." }, { - "fixture_value": 839379.0, + "fixture_value": 2987000000.0, "kind": "calibration_drift", - "ledger_value": 826160.8333333334, - "name": "dwp.uc.households_couple_with_children", + "ledger_value": 3400000000.0, + "name": "hmrc.salary_sacrifice.nics_relief_employer", "period": 2025, - "reason": "Inherited PR #891 contract: current paid-UC counts use the explicit January-December 2025 source windows on the ec7169 national feed. This compares that declaration with the frozen 2025 incumbent baseline; it does not redatum the observations to the comparison year or create a new year ruling." + "reason": "Ledger-compiled value differs from the fixture value at this comparison period." }, { - "fixture_value": 2868011.0, + "fixture_value": 3379200.0, "kind": "calibration_drift", - "ledger_value": 2990070.1666666665, - "name": "dwp.uc.households_single_no_children", + "ledger_value": 3300000.0, + "name": "hmrc.salary_sacrifice.users_above_cap", "period": 2025, - "reason": "Inherited PR #891 contract: current paid-UC counts use the explicit January-December 2025 source windows on the ec7169 national feed. This compares that declaration with the frozen 2025 incumbent baseline; it does not redatum the observations to the comparison year or create a new year ruling." + "reason": "Ledger-compiled value differs from the fixture value at this comparison period." }, { - "fixture_value": 2156879.0, + "fixture_value": 4403200.0, "kind": "calibration_drift", - "ledger_value": 2138780.1666666665, - "name": "dwp.uc.households_single_with_children", + "ledger_value": 4300000.0, + "name": "hmrc.salary_sacrifice.users_below_cap", "period": 2025, - "reason": "Inherited PR #891 contract: current paid-UC counts use the explicit January-December 2025 source windows on the ec7169 national feed. This compares that declaration with the frozen 2025 incumbent baseline; it does not redatum the observations to the comparison year or create a new year ruling." + "reason": "Ledger-compiled value differs from the fixture value at this comparison period." }, { - "fixture_value": 14000.0, + "fixture_value": 7884800.0, "kind": "calibration_drift", - "ledger_value": 14333.222222222223, - "name": "dwp.uc.scotland_households_child_under_1", + "ledger_value": 7700000.0, + "name": "hmrc.salary_sacrifice.users_total", "period": 2025, "reason": "Ledger-compiled value differs from the fixture value at this comparison period." }, { - "fixture_value": 7610.0, + "fixture_value": 557000.0, "kind": "calibration_drift", - "ledger_value": 7506.333333333333, - "name": "dwp/uc_payment_dist/COUPLE_NO_CHILDREN_annual_payment_0_to_1_200", + "ledger_value": 556551.0, + "name": "isc.private_school_students", "period": 2025, "reason": "Ledger-compiled value differs from the fixture value at this comparison period." }, { - "fixture_value": 12370.0, + "fixture_value": 2057128451236.5015, "kind": "calibration_drift", - "ledger_value": 13313.333333333334, - "name": "dwp/uc_payment_dist/COUPLE_NO_CHILDREN_annual_payment_10_800_to_12_000", + "ledger_value": 2103053000000.0, + "name": "ons.land.corporate_land_value", "period": 2025, "reason": "Ledger-compiled value differs from the fixture value at this comparison period." }, { - "fixture_value": 15175.0, + "fixture_value": 5042871548763.499, "kind": "calibration_drift", - "ledger_value": 16121.444444444445, - "name": "dwp/uc_payment_dist/COUPLE_NO_CHILDREN_annual_payment_12_000_to_13_200", + "ledger_value": 4559831000000.0, + "name": "ons.land.household_land_value", "period": 2025, "reason": "Ledger-compiled value differs from the fixture value at this comparison period." }, { - "fixture_value": 12297.0, + "fixture_value": 7100000000000.0, "kind": "calibration_drift", - "ledger_value": 13317.333333333334, - "name": "dwp/uc_payment_dist/COUPLE_NO_CHILDREN_annual_payment_13_200_to_14_400", + "ledger_value": 7117771000000.0, + "name": "ons.land.land_value", "period": 2025, "reason": "Ledger-compiled value differs from the fixture value at this comparison period." }, { - "fixture_value": 17828.0, + "fixture_value": 281000.0, "kind": "calibration_drift", - "ledger_value": 19657.333333333332, - "name": "dwp/uc_payment_dist/COUPLE_NO_CHILDREN_annual_payment_14_400_to_15_600", + "ledger_value": 285165.0, + "name": "ons.population.age_0_9_by_region@E12000001", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 14427.0, + "fixture_value": 847000.0, "kind": "calibration_drift", - "ledger_value": 16443.222222222223, - "name": "dwp/uc_payment_dist/COUPLE_NO_CHILDREN_annual_payment_15_600_to_16_800", + "ledger_value": 865892.0, + "name": "ons.population.age_0_9_by_region@E12000002", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 11892.0, + "fixture_value": 618000.0, "kind": "calibration_drift", - "ledger_value": 13441.111111111111, - "name": "dwp/uc_payment_dist/COUPLE_NO_CHILDREN_annual_payment_16_800_to_18_000", + "ledger_value": 626938.0, + "name": "ons.population.age_0_9_by_region@E12000003", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 9463.0, + "fixture_value": 532000.0, "kind": "calibration_drift", - "ledger_value": 10728.555555555555, - "name": "dwp/uc_payment_dist/COUPLE_NO_CHILDREN_annual_payment_18_000_to_19_200", + "ledger_value": 543004.0, + "name": "ons.population.age_0_9_by_region@E12000004", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 8443.0, + "fixture_value": 700000.0, "kind": "calibration_drift", - "ledger_value": 9653.222222222223, - "name": "dwp/uc_payment_dist/COUPLE_NO_CHILDREN_annual_payment_19_200_to_20_400", + "ledger_value": 718837.0, + "name": "ons.population.age_0_9_by_region@E12000005", + "period": 2025, + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." + }, + { + "fixture_value": 726000.0, + "kind": "calibration_drift", + "ledger_value": 744698.0, + "name": "ons.population.age_0_9_by_region@E12000006", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 8906.0, + "fixture_value": 1042000.0, "kind": "calibration_drift", - "ledger_value": 8797.0, - "name": "dwp/uc_payment_dist/COUPLE_NO_CHILDREN_annual_payment_1_200_to_2_400", + "ledger_value": 1054493.0, + "name": "ons.population.age_0_9_by_region@E12000007", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 5770.0, + "fixture_value": 1039000.0, "kind": "calibration_drift", - "ledger_value": 6486.888888888889, - "name": "dwp/uc_payment_dist/COUPLE_NO_CHILDREN_annual_payment_20_400_to_21_600", + "ledger_value": 1059434.0, + "name": "ons.population.age_0_9_by_region@E12000008", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 3485.0, + "fixture_value": 576000.0, "kind": "calibration_drift", - "ledger_value": 3981.8888888888887, - "name": "dwp/uc_payment_dist/COUPLE_NO_CHILDREN_annual_payment_21_600_to_22_800", + "ledger_value": 575506.0, + "name": "ons.population.age_0_9_by_region@E12000009", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 2360.0, + "fixture_value": 234000.0, "kind": "calibration_drift", - "ledger_value": 2761.4444444444443, - "name": "dwp/uc_payment_dist/COUPLE_NO_CHILDREN_annual_payment_22_800_to_24_000", + "ledger_value": 229505.0, + "name": "ons.population.age_0_9_by_region@N92000002", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 1815.0, + "fixture_value": 526000.0, "kind": "calibration_drift", - "ledger_value": 2131.222222222222, - "name": "dwp/uc_payment_dist/COUPLE_NO_CHILDREN_annual_payment_24_000_to_25_200", + "ledger_value": 527989.0, + "name": "ons.population.age_0_9_by_region@S92000003", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 1186.0, + "fixture_value": 325000.0, "kind": "calibration_drift", - "ledger_value": 1475.3333333333333, - "name": "dwp/uc_payment_dist/COUPLE_NO_CHILDREN_annual_payment_25_200_to_26_400", + "ledger_value": 321552.0, + "name": "ons.population.age_0_9_by_region@W92000004", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 935.0, + "fixture_value": 311000.0, "kind": "calibration_drift", - "ledger_value": 1073.7777777777778, - "name": "dwp/uc_payment_dist/COUPLE_NO_CHILDREN_annual_payment_26_400_to_27_600", + "ledger_value": 326306.0, + "name": "ons.population.age_10_19_by_region@E12000001", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 632.0, + "fixture_value": 901000.0, "kind": "calibration_drift", - "ledger_value": 746.3333333333334, - "name": "dwp/uc_payment_dist/COUPLE_NO_CHILDREN_annual_payment_27_600_to_28_800", + "ledger_value": 952864.0, + "name": "ons.population.age_10_19_by_region@E12000002", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 580.0, + "fixture_value": 666000.0, "kind": "calibration_drift", - "ledger_value": 605.7777777777778, - "name": "dwp/uc_payment_dist/COUPLE_NO_CHILDREN_annual_payment_28_800_to_30_000", + "ledger_value": 696780.0, + "name": "ons.population.age_10_19_by_region@E12000003", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 11750.0, + "fixture_value": 585000.0, "kind": "calibration_drift", - "ledger_value": 12256.0, - "name": "dwp/uc_payment_dist/COUPLE_NO_CHILDREN_annual_payment_2_400_to_3_600", + "ledger_value": 615630.0, + "name": "ons.population.age_10_19_by_region@E12000004", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 10802.0, + "fixture_value": 743000.0, "kind": "calibration_drift", - "ledger_value": 10945.444444444445, - "name": "dwp/uc_payment_dist/COUPLE_NO_CHILDREN_annual_payment_3_600_to_4_800", + "ledger_value": 785062.0, + "name": "ons.population.age_10_19_by_region@E12000005", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 12168.0, + "fixture_value": 741000.0, "kind": "calibration_drift", - "ledger_value": 12492.444444444445, - "name": "dwp/uc_payment_dist/COUPLE_NO_CHILDREN_annual_payment_4_800_to_6_000", + "ledger_value": 785091.0, + "name": "ons.population.age_10_19_by_region@E12000006", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 12461.0, + "fixture_value": 1038000.0, "kind": "calibration_drift", - "ledger_value": 12979.666666666666, - "name": "dwp/uc_payment_dist/COUPLE_NO_CHILDREN_annual_payment_6_000_to_7_200", + "ledger_value": 1068691.0, + "name": "ons.population.age_10_19_by_region@E12000007", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 15787.0, + "fixture_value": 1114000.0, "kind": "calibration_drift", - "ledger_value": 16484.444444444445, - "name": "dwp/uc_payment_dist/COUPLE_NO_CHILDREN_annual_payment_7_200_to_8_400", + "ledger_value": 1183235.0, + "name": "ons.population.age_10_19_by_region@E12000008", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 14248.0, + "fixture_value": 646000.0, "kind": "calibration_drift", - "ledger_value": 15272.444444444445, - "name": "dwp/uc_payment_dist/COUPLE_NO_CHILDREN_annual_payment_8_400_to_9_600", + "ledger_value": 677228.0, + "name": "ons.population.age_10_19_by_region@E12000009", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 17559.0, + "fixture_value": 244000.0, "kind": "calibration_drift", - "ledger_value": 19142.333333333332, - "name": "dwp/uc_payment_dist/COUPLE_NO_CHILDREN_annual_payment_9_600_to_10_800", + "ledger_value": 252764.0, + "name": "ons.population.age_10_19_by_region@N92000002", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 18714.0, + "fixture_value": 597000.0, "kind": "calibration_drift", - "ledger_value": 19073.555555555555, - "name": "dwp/uc_payment_dist/COUPLE_WITH_CHILDREN_annual_payment_0_to_1_200", + "ledger_value": 612935.0, + "name": "ons.population.age_10_19_by_region@S92000003", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 42188.0, + "fixture_value": 362000.0, "kind": "calibration_drift", - "ledger_value": 40805.444444444445, - "name": "dwp/uc_payment_dist/COUPLE_WITH_CHILDREN_annual_payment_10_800_to_12_000", + "ledger_value": 374851.0, + "name": "ons.population.age_10_19_by_region@W92000004", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 41520.0, + "fixture_value": 343000.0, "kind": "calibration_drift", - "ledger_value": 40009.555555555555, - "name": "dwp/uc_payment_dist/COUPLE_WITH_CHILDREN_annual_payment_12_000_to_13_200", + "ledger_value": 343015.0, + "name": "ons.population.age_20_29_by_region@E12000001", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 41159.0, + "fixture_value": 986000.0, "kind": "calibration_drift", - "ledger_value": 39972.555555555555, - "name": "dwp/uc_payment_dist/COUPLE_WITH_CHILDREN_annual_payment_13_200_to_14_400", + "ledger_value": 986590.0, + "name": "ons.population.age_20_29_by_region@E12000002", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 42515.0, + "fixture_value": 733000.0, "kind": "calibration_drift", - "ledger_value": 40518.77777777778, - "name": "dwp/uc_payment_dist/COUPLE_WITH_CHILDREN_annual_payment_14_400_to_15_600", + "ledger_value": 717479.0, + "name": "ons.population.age_20_29_by_region@E12000003", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 38527.0, + "fixture_value": 632000.0, "kind": "calibration_drift", - "ledger_value": 37118.0, - "name": "dwp/uc_payment_dist/COUPLE_WITH_CHILDREN_annual_payment_15_600_to_16_800", + "ledger_value": 612108.0, + "name": "ons.population.age_20_29_by_region@E12000004", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 38894.0, + "fixture_value": 774000.0, "kind": "calibration_drift", - "ledger_value": 37177.77777777778, - "name": "dwp/uc_payment_dist/COUPLE_WITH_CHILDREN_annual_payment_16_800_to_18_000", + "ledger_value": 766058.0, + "name": "ons.population.age_20_29_by_region@E12000005", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 38659.0, + "fixture_value": 749000.0, "kind": "calibration_drift", - "ledger_value": 37164.555555555555, - "name": "dwp/uc_payment_dist/COUPLE_WITH_CHILDREN_annual_payment_18_000_to_19_200", + "ledger_value": 727218.0, + "name": "ons.population.age_20_29_by_region@E12000006", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 34318.0, + "fixture_value": 1470000.0, "kind": "calibration_drift", - "ledger_value": 33309.555555555555, - "name": "dwp/uc_payment_dist/COUPLE_WITH_CHILDREN_annual_payment_19_200_to_20_400", + "ledger_value": 1508607.0, + "name": "ons.population.age_20_29_by_region@E12000007", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 22489.0, + "fixture_value": 1093000.0, "kind": "calibration_drift", - "ledger_value": 22775.777777777777, - "name": "dwp/uc_payment_dist/COUPLE_WITH_CHILDREN_annual_payment_1_200_to_2_400", + "ledger_value": 1066121.0, + "name": "ons.population.age_20_29_by_region@E12000008", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 30116.0, + "fixture_value": 694000.0, "kind": "calibration_drift", - "ledger_value": 29102.555555555555, - "name": "dwp/uc_payment_dist/COUPLE_WITH_CHILDREN_annual_payment_20_400_to_21_600", + "ledger_value": 684393.0, + "name": "ons.population.age_20_29_by_region@E12000009", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 28519.0, + "fixture_value": 230000.0, "kind": "calibration_drift", - "ledger_value": 27534.666666666668, - "name": "dwp/uc_payment_dist/COUPLE_WITH_CHILDREN_annual_payment_21_600_to_22_800", + "ledger_value": 219525.0, + "name": "ons.population.age_20_29_by_region@N92000002", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 26014.0, + "fixture_value": 709000.0, "kind": "calibration_drift", - "ledger_value": 25584.777777777777, - "name": "dwp/uc_payment_dist/COUPLE_WITH_CHILDREN_annual_payment_22_800_to_24_000", + "ledger_value": 703554.0, + "name": "ons.population.age_20_29_by_region@S92000003", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 23159.0, + "fixture_value": 389000.0, "kind": "calibration_drift", - "ledger_value": 22927.777777777777, - "name": "dwp/uc_payment_dist/COUPLE_WITH_CHILDREN_annual_payment_24_000_to_25_200", + "ledger_value": 380557.0, + "name": "ons.population.age_20_29_by_region@W92000004", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 20618.0, + "fixture_value": 356000.0, "kind": "calibration_drift", - "ledger_value": 20334.666666666668, - "name": "dwp/uc_payment_dist/COUPLE_WITH_CHILDREN_annual_payment_25_200_to_26_400", + "ledger_value": 363357.0, + "name": "ons.population.age_30_39_by_region@E12000001", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 17493.0, + "fixture_value": 1056000.0, "kind": "calibration_drift", - "ledger_value": 17535.0, - "name": "dwp/uc_payment_dist/COUPLE_WITH_CHILDREN_annual_payment_26_400_to_27_600", + "ledger_value": 1064968.0, + "name": "ons.population.age_30_39_by_region@E12000002", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 15437.0, + "fixture_value": 764000.0, "kind": "calibration_drift", - "ledger_value": 15399.888888888889, - "name": "dwp/uc_payment_dist/COUPLE_WITH_CHILDREN_annual_payment_27_600_to_28_800", + "ledger_value": 770809.0, + "name": "ons.population.age_30_39_by_region@E12000003", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 12836.0, + "fixture_value": 669000.0, "kind": "calibration_drift", - "ledger_value": 12836.222222222223, - "name": "dwp/uc_payment_dist/COUPLE_WITH_CHILDREN_annual_payment_28_800_to_30_000", + "ledger_value": 676709.0, + "name": "ons.population.age_30_39_by_region@E12000004", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 26734.0, + "fixture_value": 834000.0, "kind": "calibration_drift", - "ledger_value": 26502.666666666668, - "name": "dwp/uc_payment_dist/COUPLE_WITH_CHILDREN_annual_payment_2_400_to_3_600", + "ledger_value": 839114.0, + "name": "ons.population.age_30_39_by_region@E12000005", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 29189.0, + "fixture_value": 890000.0, "kind": "calibration_drift", - "ledger_value": 29027.0, - "name": "dwp/uc_payment_dist/COUPLE_WITH_CHILDREN_annual_payment_3_600_to_4_800", + "ledger_value": 895639.0, + "name": "ons.population.age_30_39_by_region@E12000006", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 33137.0, + "fixture_value": 1585000.0, "kind": "calibration_drift", - "ledger_value": 32427.0, - "name": "dwp/uc_payment_dist/COUPLE_WITH_CHILDREN_annual_payment_4_800_to_6_000", + "ledger_value": 1537372.0, + "name": "ons.population.age_30_39_by_region@E12000007", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 35834.0, + "fixture_value": 1283000.0, "kind": "calibration_drift", - "ledger_value": 34956.88888888889, - "name": "dwp/uc_payment_dist/COUPLE_WITH_CHILDREN_annual_payment_6_000_to_7_200", + "ledger_value": 1284552.0, + "name": "ons.population.age_30_39_by_region@E12000008", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 38338.0, + "fixture_value": 743000.0, "kind": "calibration_drift", - "ledger_value": 37410.77777777778, - "name": "dwp/uc_payment_dist/COUPLE_WITH_CHILDREN_annual_payment_7_200_to_8_400", + "ledger_value": 742082.0, + "name": "ons.population.age_30_39_by_region@E12000009", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 40685.0, + "fixture_value": 264000.0, "kind": "calibration_drift", - "ledger_value": 39910.333333333336, - "name": "dwp/uc_payment_dist/COUPLE_WITH_CHILDREN_annual_payment_8_400_to_9_600", + "ledger_value": 254067.0, + "name": "ons.population.age_30_39_by_region@N92000002", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 40564.0, + "fixture_value": 736000.0, "kind": "calibration_drift", - "ledger_value": 39368.0, - "name": "dwp/uc_payment_dist/COUPLE_WITH_CHILDREN_annual_payment_9_600_to_10_800", + "ledger_value": 733480.0, + "name": "ons.population.age_30_39_by_region@S92000003", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 24639.0, + "fixture_value": 408000.0, "kind": "calibration_drift", - "ledger_value": 26670.222222222223, - "name": "dwp/uc_payment_dist/LONE_PARENT_annual_payment_0_to_1_200", + "ledger_value": 410248.0, + "name": "ons.population.age_30_39_by_region@W92000004", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 126949.0, + "fixture_value": 323000.0, "kind": "calibration_drift", - "ledger_value": 123718.11111111111, - "name": "dwp/uc_payment_dist/LONE_PARENT_annual_payment_10_800_to_12_000", + "ledger_value": 320860.0, + "name": "ons.population.age_40_49_by_region@E12000001", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 131129.0, + "fixture_value": 938000.0, "kind": "calibration_drift", - "ledger_value": 125798.44444444444, - "name": "dwp/uc_payment_dist/LONE_PARENT_annual_payment_12_000_to_13_200", + "ledger_value": 930967.0, + "name": "ons.population.age_40_49_by_region@E12000002", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 131289.0, + "fixture_value": 686000.0, "kind": "calibration_drift", - "ledger_value": 129133.66666666667, - "name": "dwp/uc_payment_dist/LONE_PARENT_annual_payment_13_200_to_14_400", + "ledger_value": 674349.0, + "name": "ons.population.age_40_49_by_region@E12000003", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 119355.0, + "fixture_value": 621000.0, "kind": "calibration_drift", - "ledger_value": 115856.66666666667, - "name": "dwp/uc_payment_dist/LONE_PARENT_annual_payment_14_400_to_15_600", + "ledger_value": 610611.0, + "name": "ons.population.age_40_49_by_region@E12000004", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 130802.0, + "fixture_value": 758000.0, "kind": "calibration_drift", - "ledger_value": 125660.22222222222, - "name": "dwp/uc_payment_dist/LONE_PARENT_annual_payment_15_600_to_16_800", + "ledger_value": 746341.0, + "name": "ons.population.age_40_49_by_region@E12000005", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 141848.0, + "fixture_value": 851000.0, "kind": "calibration_drift", - "ledger_value": 135836.77777777778, - "name": "dwp/uc_payment_dist/LONE_PARENT_annual_payment_16_800_to_18_000", + "ledger_value": 838578.0, + "name": "ons.population.age_40_49_by_region@E12000006", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 136835.0, + "fixture_value": 1314000.0, "kind": "calibration_drift", - "ledger_value": 135444.88888888888, - "name": "dwp/uc_payment_dist/LONE_PARENT_annual_payment_18_000_to_19_200", + "ledger_value": 1268973.0, + "name": "ons.population.age_40_49_by_region@E12000007", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 107556.0, + "fixture_value": 1273000.0, "kind": "calibration_drift", - "ledger_value": 106341.88888888889, - "name": "dwp/uc_payment_dist/LONE_PARENT_annual_payment_19_200_to_20_400", + "ledger_value": 1255951.0, + "name": "ons.population.age_40_49_by_region@E12000008", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 34670.0, + "fixture_value": 704000.0, "kind": "calibration_drift", - "ledger_value": 36208.11111111111, - "name": "dwp/uc_payment_dist/LONE_PARENT_annual_payment_1_200_to_2_400", + "ledger_value": 688417.0, + "name": "ons.population.age_40_49_by_region@E12000009", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 86616.0, + "fixture_value": 254000.0, "kind": "calibration_drift", - "ledger_value": 85490.44444444444, - "name": "dwp/uc_payment_dist/LONE_PARENT_annual_payment_20_400_to_21_600", + "ledger_value": 246140.0, + "name": "ons.population.age_40_49_by_region@N92000002", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 80294.0, + "fixture_value": 691000.0, "kind": "calibration_drift", - "ledger_value": 80226.88888888889, - "name": "dwp/uc_payment_dist/LONE_PARENT_annual_payment_21_600_to_22_800", + "ledger_value": 675368.0, + "name": "ons.population.age_40_49_by_region@S92000003", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 70309.0, + "fixture_value": 374000.0, "kind": "calibration_drift", - "ledger_value": 71259.55555555556, - "name": "dwp/uc_payment_dist/LONE_PARENT_annual_payment_22_800_to_24_000", + "ledger_value": 365951.0, + "name": "ons.population.age_40_49_by_region@W92000004", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 50510.0, + "fixture_value": 369000.0, "kind": "calibration_drift", - "ledger_value": 51301.666666666664, - "name": "dwp/uc_payment_dist/LONE_PARENT_annual_payment_24_000_to_25_200", + "ledger_value": 360074.0, + "name": "ons.population.age_50_59_by_region@E12000001", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 42522.0, + "fixture_value": 1011000.0, "kind": "calibration_drift", - "ledger_value": 43305.22222222222, - "name": "dwp/uc_payment_dist/LONE_PARENT_annual_payment_25_200_to_26_400", + "ledger_value": 995810.0, + "name": "ons.population.age_50_59_by_region@E12000002", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 33508.0, + "fixture_value": 745000.0, "kind": "calibration_drift", - "ledger_value": 34910.555555555555, - "name": "dwp/uc_payment_dist/LONE_PARENT_annual_payment_26_400_to_27_600", + "ledger_value": 736903.0, + "name": "ons.population.age_50_59_by_region@E12000003", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 27063.0, + "fixture_value": 681000.0, "kind": "calibration_drift", - "ledger_value": 28513.0, - "name": "dwp/uc_payment_dist/LONE_PARENT_annual_payment_27_600_to_28_800", + "ledger_value": 676307.0, + "name": "ons.population.age_50_59_by_region@E12000004", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 21663.0, + "fixture_value": 800000.0, "kind": "calibration_drift", - "ledger_value": 22593.0, - "name": "dwp/uc_payment_dist/LONE_PARENT_annual_payment_28_800_to_30_000", + "ledger_value": 794143.0, + "name": "ons.population.age_50_59_by_region@E12000005", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 43623.0, + "fixture_value": 867000.0, "kind": "calibration_drift", - "ledger_value": 46312.77777777778, - "name": "dwp/uc_payment_dist/LONE_PARENT_annual_payment_2_400_to_3_600", + "ledger_value": 866946.0, + "name": "ons.population.age_50_59_by_region@E12000006", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 55622.0, + "fixture_value": 1078000.0, "kind": "calibration_drift", - "ledger_value": 60310.11111111111, - "name": "dwp/uc_payment_dist/LONE_PARENT_annual_payment_3_600_to_4_800", + "ledger_value": 1087518.0, + "name": "ons.population.age_50_59_by_region@E12000007", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 67901.0, + "fixture_value": 1287000.0, "kind": "calibration_drift", - "ledger_value": 67858.66666666667, - "name": "dwp/uc_payment_dist/LONE_PARENT_annual_payment_4_800_to_6_000", + "ledger_value": 1287719.0, + "name": "ons.population.age_50_59_by_region@E12000008", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 89314.0, + "fixture_value": 794000.0, "kind": "calibration_drift", - "ledger_value": 89577.44444444444, - "name": "dwp/uc_payment_dist/LONE_PARENT_annual_payment_6_000_to_7_200", + "ledger_value": 784718.0, + "name": "ons.population.age_50_59_by_region@E12000009", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 118641.0, + "fixture_value": 257000.0, "kind": "calibration_drift", - "ledger_value": 116076.0, - "name": "dwp/uc_payment_dist/LONE_PARENT_annual_payment_7_200_to_8_400", + "ledger_value": 255269.0, + "name": "ons.population.age_50_59_by_region@N92000002", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 99069.0, + "fixture_value": 788000.0, "kind": "calibration_drift", - "ledger_value": 97511.0, - "name": "dwp/uc_payment_dist/LONE_PARENT_annual_payment_8_400_to_9_600", + "ledger_value": 766715.0, + "name": "ons.population.age_50_59_by_region@S92000003", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 102251.0, + "fixture_value": 432000.0, "kind": "calibration_drift", - "ledger_value": 102343.0, - "name": "dwp/uc_payment_dist/LONE_PARENT_annual_payment_9_600_to_10_800", + "ledger_value": 424694.0, + "name": "ons.population.age_50_59_by_region@W92000004", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 102312.0, + "fixture_value": 353000.0, "kind": "calibration_drift", - "ledger_value": 89479.33333333333, - "name": "dwp/uc_payment_dist/SINGLE_annual_payment_0_to_1_200", + "ledger_value": 349719.0, + "name": "ons.population.age_60_69_by_region@E12000001", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 95698.0, + "fixture_value": 888000.0, "kind": "calibration_drift", - "ledger_value": 104891.11111111111, - "name": "dwp/uc_payment_dist/SINGLE_annual_payment_10_800_to_12_000", + "ledger_value": 884043.0, + "name": "ons.population.age_60_69_by_region@E12000002", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 106663.0, + "fixture_value": 658000.0, "kind": "calibration_drift", - "ledger_value": 122035.55555555556, - "name": "dwp/uc_payment_dist/SINGLE_annual_payment_12_000_to_13_200", + "ledger_value": 652931.0, + "name": "ons.population.age_60_69_by_region@E12000003", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 181180.0, + "fixture_value": 592000.0, "kind": "calibration_drift", - "ledger_value": 197163.22222222222, - "name": "dwp/uc_payment_dist/SINGLE_annual_payment_13_200_to_14_400", + "ledger_value": 591429.0, + "name": "ons.population.age_60_69_by_region@E12000004", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 189861.0, + "fixture_value": 685000.0, "kind": "calibration_drift", - "ledger_value": 209497.33333333334, - "name": "dwp/uc_payment_dist/SINGLE_annual_payment_14_400_to_15_600", + "ledger_value": 677922.0, + "name": "ons.population.age_60_69_by_region@E12000005", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 129174.0, + "fixture_value": 746000.0, "kind": "calibration_drift", - "ledger_value": 149963.22222222222, - "name": "dwp/uc_payment_dist/SINGLE_annual_payment_15_600_to_16_800", + "ledger_value": 744651.0, + "name": "ons.population.age_60_69_by_region@E12000006", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 84690.0, + "fixture_value": 778000.0, "kind": "calibration_drift", - "ledger_value": 108689.66666666667, - "name": "dwp/uc_payment_dist/SINGLE_annual_payment_16_800_to_18_000", + "ledger_value": 787666.0, + "name": "ons.population.age_60_69_by_region@E12000007", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 46244.0, + "fixture_value": 1089000.0, "kind": "calibration_drift", - "ledger_value": 61718.666666666664, - "name": "dwp/uc_payment_dist/SINGLE_annual_payment_18_000_to_19_200", + "ledger_value": 1089421.0, + "name": "ons.population.age_60_69_by_region@E12000008", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 26481.0, + "fixture_value": 741000.0, "kind": "calibration_drift", - "ledger_value": 34050.444444444445, - "name": "dwp/uc_payment_dist/SINGLE_annual_payment_19_200_to_20_400", + "ledger_value": 736639.0, + "name": "ons.population.age_60_69_by_region@E12000009", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 120819.0, + "fixture_value": 224000.0, "kind": "calibration_drift", - "ledger_value": 127403.22222222222, - "name": "dwp/uc_payment_dist/SINGLE_annual_payment_1_200_to_2_400", + "ledger_value": 223111.0, + "name": "ons.population.age_60_69_by_region@N92000002", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 18143.0, + "fixture_value": 727000.0, "kind": "calibration_drift", - "ledger_value": 22338.333333333332, - "name": "dwp/uc_payment_dist/SINGLE_annual_payment_20_400_to_21_600", + "ledger_value": 721727.0, + "name": "ons.population.age_60_69_by_region@S92000003", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 12325.0, + "fixture_value": 407000.0, "kind": "calibration_drift", - "ledger_value": 14685.777777777777, - "name": "dwp/uc_payment_dist/SINGLE_annual_payment_21_600_to_22_800", + "ledger_value": 401775.0, + "name": "ons.population.age_60_69_by_region@W92000004", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 9555.0, + "fixture_value": 278000.0, "kind": "calibration_drift", - "ledger_value": 11133.666666666666, - "name": "dwp/uc_payment_dist/SINGLE_annual_payment_22_800_to_24_000", + "ledger_value": 265573.0, + "name": "ons.population.age_70_79_by_region@E12000001", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 6230.0, + "fixture_value": 715000.0, "kind": "calibration_drift", - "ledger_value": 6951.222222222223, - "name": "dwp/uc_payment_dist/SINGLE_annual_payment_24_000_to_25_200", + "ledger_value": 669277.0, + "name": "ons.population.age_70_79_by_region@E12000002", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 5884.0, + "fixture_value": 536000.0, "kind": "calibration_drift", - "ledger_value": 6508.222222222223, - "name": "dwp/uc_payment_dist/SINGLE_annual_payment_25_200_to_26_400", + "ledger_value": 504131.0, + "name": "ons.population.age_70_79_by_region@E12000003", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 5362.0, + "fixture_value": 500000.0, "kind": "calibration_drift", - "ledger_value": 5913.444444444444, - "name": "dwp/uc_payment_dist/SINGLE_annual_payment_26_400_to_27_600", + "ledger_value": 466530.0, + "name": "ons.population.age_70_79_by_region@E12000004", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 1786.0, + "fixture_value": 570000.0, "kind": "calibration_drift", - "ledger_value": 1884.111111111111, - "name": "dwp/uc_payment_dist/SINGLE_annual_payment_27_600_to_28_800", + "ledger_value": 528207.0, + "name": "ons.population.age_70_79_by_region@E12000005", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 1491.0, + "fixture_value": 643000.0, "kind": "calibration_drift", - "ledger_value": 1653.7777777777778, - "name": "dwp/uc_payment_dist/SINGLE_annual_payment_28_800_to_30_000", + "ledger_value": 599882.0, + "name": "ons.population.age_70_79_by_region@E12000006", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 173925.0, + "fixture_value": 509000.0, "kind": "calibration_drift", - "ledger_value": 197136.66666666666, - "name": "dwp/uc_payment_dist/SINGLE_annual_payment_2_400_to_3_600", + "ledger_value": 485057.0, + "name": "ons.population.age_70_79_by_region@E12000007", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 375706.0, + "fixture_value": 927000.0, "kind": "calibration_drift", - "ledger_value": 377063.1111111111, - "name": "dwp/uc_payment_dist/SINGLE_annual_payment_3_600_to_4_800", + "ledger_value": 865392.0, + "name": "ons.population.age_70_79_by_region@E12000008", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 200713.0, + "fixture_value": 662000.0, "kind": "calibration_drift", - "ledger_value": 195983.22222222222, - "name": "dwp/uc_payment_dist/SINGLE_annual_payment_4_800_to_6_000", + "ledger_value": 618254.0, + "name": "ons.population.age_70_79_by_region@E12000009", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 107860.0, + "fixture_value": 165000.0, "kind": "calibration_drift", - "ledger_value": 114033.33333333333, - "name": "dwp/uc_payment_dist/SINGLE_annual_payment_6_000_to_7_200", + "ledger_value": 155382.0, + "name": "ons.population.age_70_79_by_region@N92000002", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 144036.0, + "fixture_value": 546000.0, "kind": "calibration_drift", - "ledger_value": 149043.77777777778, - "name": "dwp/uc_payment_dist/SINGLE_annual_payment_7_200_to_8_400", + "ledger_value": 519252.0, + "name": "ons.population.age_70_79_by_region@S92000003", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 377221.0, + "fixture_value": 343000.0, "kind": "calibration_drift", - "ledger_value": 399750.22222222225, - "name": "dwp/uc_payment_dist/SINGLE_annual_payment_8_400_to_9_600", + "ledger_value": 321668.0, + "name": "ons.population.age_70_79_by_region@W92000004", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 341138.0, + "fixture_value": 153000.0, "kind": "calibration_drift", - "ledger_value": 386411.6666666667, - "name": "dwp/uc_payment_dist/SINGLE_annual_payment_9_600_to_10_800", + "ledger_value": 122118.0, + "name": "ons.population.age_80_89_by_region@E12000001", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 67727478991.59665, + "fixture_value": 399000.0, "kind": "calibration_drift", - "ledger_value": 119258000000.0, - "name": "hmrc.cgt.gains_total", + "ledger_value": 324141.0, + "name": "ons.population.age_80_89_by_region@E12000002", "period": 2025, - "reason": "PR #889 selects HMRC FY2024-25 individuals-only observed gains of GBP 119,258,000,000, measured in 2024 at calibration index 2025. The frozen incumbent fixture retains its historical forecast/uprated GBP 67,727,478,991.60 value. This explicit observation-year and population-scope change does not rewrite the historical fixture." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 378000.0, + "fixture_value": 302000.0, "kind": "calibration_drift", - "ledger_value": 551000.0, - "name": "hmrc.cgt.taxpayers_total", + "ledger_value": 243274.0, + "name": "ons.population.age_80_89_by_region@E12000003", "period": 2025, - "reason": "PR #889 selects the HMRC FY2024-25 individuals-only observation of 551,000 taxpayers, measured in 2024 at calibration index 2025. The frozen incumbent fixture retains its historical 378,000 count." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 1236000000.0, + "fixture_value": 273000.0, "kind": "calibration_drift", - "ledger_value": 1000000000.0, - "name": "hmrc.salary_sacrifice.nics_relief_employee", + "ledger_value": 225626.0, + "name": "ons.population.age_80_89_by_region@E12000004", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 2987000000.0, + "fixture_value": 337000.0, "kind": "calibration_drift", - "ledger_value": 3400000000.0, - "name": "hmrc.salary_sacrifice.nics_relief_employer", + "ledger_value": 275030.0, + "name": "ons.population.age_80_89_by_region@E12000005", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 3379200.0, + "fixture_value": 379000.0, "kind": "calibration_drift", - "ledger_value": 3300000.0, - "name": "hmrc.salary_sacrifice.users_above_cap", + "ledger_value": 305691.0, + "name": "ons.population.age_80_89_by_region@E12000006", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 4403200.0, + "fixture_value": 304000.0, "kind": "calibration_drift", - "ledger_value": 4300000.0, - "name": "hmrc.salary_sacrifice.users_below_cap", + "ledger_value": 237061.0, + "name": "ons.population.age_80_89_by_region@E12000007", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 7884800.0, + "fixture_value": 559000.0, "kind": "calibration_drift", - "ledger_value": 7700000.0, - "name": "hmrc.salary_sacrifice.users_total", + "ledger_value": 447958.0, + "name": "ons.population.age_80_89_by_region@E12000008", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 557000.0, + "fixture_value": 388000.0, "kind": "calibration_drift", - "ledger_value": 556551.0, - "name": "isc.private_school_students", + "ledger_value": 313605.0, + "name": "ons.population.age_80_89_by_region@E12000009", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 2057128451236.5015, + "fixture_value": 93000.0, "kind": "calibration_drift", - "ledger_value": 2103053000000.0, - "name": "ons.land.corporate_land_value", + "ledger_value": 77441.0, + "name": "ons.population.age_80_89_by_region@N92000002", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 5042871548763.499, + "fixture_value": 297000.0, "kind": "calibration_drift", - "ledger_value": 4559831000000.0, - "name": "ons.land.household_land_value", + "ledger_value": 238904.0, + "name": "ons.population.age_80_89_by_region@S92000003", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 7100000000000.0, + "fixture_value": 190000.0, "kind": "calibration_drift", - "ledger_value": 7117771000000.0, - "name": "ons.land.land_value", + "ledger_value": 154319.0, + "name": "ons.population.age_80_89_by_region@W92000004", "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { "fixture_value": 1698816.0, diff --git a/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_production_2023_signed_differences.json b/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_production_2023_signed_differences.json index d4f979495..8fdb5b16a 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_production_2023_signed_differences.json +++ b/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_production_2023_signed_differences.json @@ -1,11 +1,11 @@ { - "compiled_count": 239, + "compiled_count": 338, "counts_by_kind": { - "calibration_drift": 12, - "fixture_only": 125, - "ledger_only": 215 + "calibration_drift": 120, + "fixture_only": 17, + "ledger_only": 206 }, - "difference_count": 352, + "difference_count": 343, "differences": [ { "fixture_value": 82000.0, @@ -15,2401 +15,2446 @@ "reason": "Fixture row has no Ledger-compiled counterpart at this comparison period." }, { - 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"ledger_value": 13000.0, - "name": "hmrc/property_income_count_income_band_500_000_to_1_000_000", + "ledger_value": 23120884840.50285, + "name": "orr.rail_government_support", "period": 2023, "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." }, { "kind": "ledger_only", - "ledger_value": 397000.0, - "name": "hmrc/property_income_count_income_band_50_000_to_70_000", + "ledger_value": 438893000.0, + "name": "scotgov.bus.government_support", "period": 2023, "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." }, { "kind": "ledger_only", - "ledger_value": 226000.0, - "name": "hmrc/property_income_count_income_band_70_000_to_100_000", + "ledger_value": 97426240.0, + "name": "welshgov.bus.public_support", "period": 2023, "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." }, { - "kind": "ledger_only", - "ledger_value": 85000.0, - "name": "hmrc/self_employment_income_count_income_band_100_000_to_150_000", + "fixture_value": 557000.0, + "kind": "calibration_drift", + "ledger_value": 554243.0, + "name": "isc.private_school_students", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Ledger-compiled value differs from the fixture value at this comparison period." }, { - "kind": "ledger_only", - "ledger_value": 444000.0, - "name": "hmrc/self_employment_income_count_income_band_12_570_to_15_000", + "fixture_value": 1957550396058.5835, + "kind": "calibration_drift", + "ledger_value": 1976893000000.0, + "name": "ons.land.corporate_land_value", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Ledger-compiled value differs from the fixture value at this comparison period." }, { - "kind": "ledger_only", - "ledger_value": 40000.0, - "name": "hmrc/self_employment_income_count_income_band_150_000_to_200_000", + "fixture_value": 4798764603941.416, + "kind": "calibration_drift", + "ledger_value": 4383082000000.0, + "name": "ons.land.household_land_value", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Ledger-compiled value differs from the fixture value at this comparison period." }, { - "kind": "ledger_only", - "ledger_value": 651000.0, - "name": "hmrc/self_employment_income_count_income_band_15_000_to_20_000", + "fixture_value": 283000.0, + "kind": "calibration_drift", + "ledger_value": 282659.0, + "name": "ons.population.age_0_9_by_region@E12000001", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 11000.0, - "name": "hmrc/self_employment_income_count_income_band_1_000_000_to_inf", + "fixture_value": 851000.0, + "kind": "calibration_drift", + "ledger_value": 855531.0, + "name": "ons.population.age_0_9_by_region@E12000002", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 34000.0, - "name": "hmrc/self_employment_income_count_income_band_200_000_to_300_000", + "fixture_value": 621000.0, + "kind": "calibration_drift", + "ledger_value": 621826.0, + "name": "ons.population.age_0_9_by_region@E12000003", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 961000.0, - "name": "hmrc/self_employment_income_count_income_band_20_000_to_30_000", + "fixture_value": 535000.0, + "kind": "calibration_drift", + "ledger_value": 538271.0, + "name": "ons.population.age_0_9_by_region@E12000004", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 21000.0, - "name": "hmrc/self_employment_income_count_income_band_300_000_to_500_000", + "fixture_value": 703000.0, + "kind": "calibration_drift", + "ledger_value": 708449.0, + "name": "ons.population.age_0_9_by_region@E12000005", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 595000.0, - "name": "hmrc/self_employment_income_count_income_band_30_000_to_40_000", + "fixture_value": 730000.0, + "kind": "calibration_drift", + "ledger_value": 735229.0, + "name": "ons.population.age_0_9_by_region@E12000006", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 359000.0, - "name": "hmrc/self_employment_income_count_income_band_40_000_to_50_000", + "fixture_value": 1047000.0, + "kind": "calibration_drift", + "ledger_value": 1046493.0, + "name": "ons.population.age_0_9_by_region@E12000007", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 15000.0, - "name": "hmrc/self_employment_income_count_income_band_500_000_to_1_000_000", + "fixture_value": 1044000.0, + "kind": "calibration_drift", + "ledger_value": 1049550.0, + "name": "ons.population.age_0_9_by_region@E12000008", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 311000.0, - "name": "hmrc/self_employment_income_count_income_band_50_000_to_70_000", + "fixture_value": 579000.0, + "kind": "calibration_drift", + "ledger_value": 575396.0, + "name": "ons.population.age_0_9_by_region@E12000009", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 143000.0, - "name": "hmrc/self_employment_income_count_income_band_70_000_to_100_000", + "fixture_value": 235000.0, + "kind": "calibration_drift", + "ledger_value": 232833.0, + "name": "ons.population.age_0_9_by_region@N92000002", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 4980000000.0, - "name": "hmrc/self_employment_income_income_band_100_000_to_150_000", + "fixture_value": 528000.0, + "kind": "calibration_drift", + "ledger_value": 527966.0, + "name": "ons.population.age_0_9_by_region@S92000003", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 4810000000.0, - "name": "hmrc/self_employment_income_income_band_12_570_to_15_000", + "fixture_value": 326000.0, + "kind": "calibration_drift", + "ledger_value": 323725.0, + "name": "ons.population.age_0_9_by_region@W92000004", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 3650000000.0, - "name": "hmrc/self_employment_income_income_band_150_000_to_200_000", + "fixture_value": 312000.0, + "kind": "calibration_drift", + "ledger_value": 317901.0, + "name": "ons.population.age_10_19_by_region@E12000001", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 8030000000.0, - "name": "hmrc/self_employment_income_income_band_15_000_to_20_000", + "fixture_value": 906000.0, + "kind": "calibration_drift", + "ledger_value": 927958.0, + "name": "ons.population.age_10_19_by_region@E12000002", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 19500000000.0, - "name": "hmrc/self_employment_income_income_band_1_000_000_to_inf", + "fixture_value": 669000.0, + "kind": "calibration_drift", + "ledger_value": 683509.0, + "name": "ons.population.age_10_19_by_region@E12000003", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 4390000000.0, - "name": "hmrc/self_employment_income_income_band_200_000_to_300_000", + "fixture_value": 588000.0, + "kind": "calibration_drift", + "ledger_value": 601285.0, + "name": "ons.population.age_10_19_by_region@E12000004", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 15700000000.0, - "name": "hmrc/self_employment_income_income_band_20_000_to_30_000", + "fixture_value": 747000.0, + "kind": "calibration_drift", + "ledger_value": 765407.0, + "name": "ons.population.age_10_19_by_region@E12000005", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 4840000000.0, - "name": "hmrc/self_employment_income_income_band_300_000_to_500_000", + "fixture_value": 745000.0, + "kind": "calibration_drift", + "ledger_value": 766334.0, + "name": "ons.population.age_10_19_by_region@E12000006", + "period": 2023, + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." + }, + { + "fixture_value": 1043000.0, + "kind": "calibration_drift", + "ledger_value": 1057772.0, + "name": "ons.population.age_10_19_by_region@E12000007", + "period": 2023, + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." + }, + { + "fixture_value": 1119000.0, + "kind": "calibration_drift", + "ledger_value": 1151908.0, + "name": "ons.population.age_10_19_by_region@E12000008", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 12600000000.0, - "name": "hmrc/self_employment_income_income_band_30_000_to_40_000", + "fixture_value": 650000.0, + "kind": "calibration_drift", + "ledger_value": 661929.0, + "name": "ons.population.age_10_19_by_region@E12000009", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 8730000000.0, - "name": "hmrc/self_employment_income_income_band_40_000_to_50_000", + "fixture_value": 245000.0, + "kind": "calibration_drift", + "ledger_value": 250006.0, + "name": "ons.population.age_10_19_by_region@N92000002", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 6670000000.0, - "name": "hmrc/self_employment_income_income_band_500_000_to_1_000_000", + "fixture_value": 600000.0, + "kind": "calibration_drift", + "ledger_value": 606067.0, + "name": "ons.population.age_10_19_by_region@S92000003", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 8220000000.0, - "name": "hmrc/self_employment_income_income_band_50_000_to_70_000", + "fixture_value": 364000.0, + "kind": "calibration_drift", + "ledger_value": 371679.0, + "name": "ons.population.age_10_19_by_region@W92000004", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 5360000000.0, - "name": "hmrc/self_employment_income_income_band_70_000_to_100_000", + "fixture_value": 333000.0, + "kind": "calibration_drift", + "ledger_value": 332989.0, + "name": "ons.population.age_20_29_by_region@E12000001", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 74000.0, - "name": "hmrc/state_pension_count_income_band_100_000_to_150_000", + "fixture_value": 958000.0, + "kind": "calibration_drift", + "ledger_value": 959563.0, + "name": "ons.population.age_20_29_by_region@E12000002", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 1140000.0, - "name": "hmrc/state_pension_count_income_band_12_570_to_15_000", + "fixture_value": 713000.0, + "kind": "calibration_drift", + "ledger_value": 706311.0, + "name": "ons.population.age_20_29_by_region@E12000003", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 21000.0, - "name": "hmrc/state_pension_count_income_band_150_000_to_200_000", + "fixture_value": 614000.0, + "kind": "calibration_drift", + "ledger_value": 608545.0, + "name": "ons.population.age_20_29_by_region@E12000004", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 2050000.0, - "name": "hmrc/state_pension_count_income_band_15_000_to_20_000", + "fixture_value": 753000.0, + "kind": "calibration_drift", + "ledger_value": 746782.0, + "name": "ons.population.age_20_29_by_region@E12000005", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 2000.0, - "name": "hmrc/state_pension_count_income_band_1_000_000_to_inf", + "fixture_value": 729000.0, + "kind": "calibration_drift", + "ledger_value": 717130.0, + "name": "ons.population.age_20_29_by_region@E12000006", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 14000.0, - "name": "hmrc/state_pension_count_income_band_200_000_to_300_000", + "fixture_value": 1429000.0, + "kind": "calibration_drift", + "ledger_value": 1444048.0, + "name": "ons.population.age_20_29_by_region@E12000007", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 2320000.0, - "name": "hmrc/state_pension_count_income_band_20_000_to_30_000", + "fixture_value": 1062000.0, + "kind": "calibration_drift", + "ledger_value": 1047994.0, + "name": "ons.population.age_20_29_by_region@E12000008", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 8000.0, - "name": "hmrc/state_pension_count_income_band_300_000_to_500_000", + "fixture_value": 675000.0, + "kind": "calibration_drift", + "ledger_value": 670859.0, + "name": "ons.population.age_20_29_by_region@E12000009", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 1160000.0, - "name": "hmrc/state_pension_count_income_band_30_000_to_40_000", + "fixture_value": 224000.0, + "kind": "calibration_drift", + "ledger_value": 219166.0, + "name": "ons.population.age_20_29_by_region@N92000002", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 568000.0, - "name": "hmrc/state_pension_count_income_band_40_000_to_50_000", + "fixture_value": 689000.0, + "kind": "calibration_drift", + "ledger_value": 690464.0, + "name": "ons.population.age_20_29_by_region@S92000003", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 3000.0, - "name": "hmrc/state_pension_count_income_band_500_000_to_1_000_000", + "fixture_value": 378000.0, + "kind": "calibration_drift", + "ledger_value": 381438.0, + "name": "ons.population.age_20_29_by_region@W92000004", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 441000.0, - "name": "hmrc/state_pension_count_income_band_50_000_to_70_000", + "fixture_value": 347000.0, + "kind": "calibration_drift", + "ledger_value": 350720.0, + "name": "ons.population.age_30_39_by_region@E12000001", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 176000.0, - "name": "hmrc/state_pension_count_income_band_70_000_to_100_000", + "fixture_value": 1029000.0, + "kind": "calibration_drift", + "ledger_value": 1032427.0, + "name": "ons.population.age_30_39_by_region@E12000002", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 847000000.0, - "name": "hmrc/state_pension_income_band_100_000_to_150_000", + "fixture_value": 745000.0, + "kind": "calibration_drift", + "ledger_value": 749846.0, + "name": "ons.population.age_30_39_by_region@E12000003", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 11800000000.0, - "name": "hmrc/state_pension_income_band_12_570_to_15_000", + "fixture_value": 652000.0, + "kind": "calibration_drift", + "ledger_value": 656771.0, + "name": "ons.population.age_30_39_by_region@E12000004", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 265000000.0, - "name": "hmrc/state_pension_income_band_150_000_to_200_000", + "fixture_value": 813000.0, + "kind": "calibration_drift", + "ledger_value": 814710.0, + "name": "ons.population.age_30_39_by_region@E12000005", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 22200000000.0, - "name": "hmrc/state_pension_income_band_15_000_to_20_000", + "fixture_value": 868000.0, + "kind": "calibration_drift", + "ledger_value": 869971.0, + "name": "ons.population.age_30_39_by_region@E12000006", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 20000000.0, - "name": "hmrc/state_pension_income_band_1_000_000_to_inf", + "fixture_value": 1545000.0, + "kind": "calibration_drift", + "ledger_value": 1516452.0, + "name": "ons.population.age_30_39_by_region@E12000007", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 179000000.0, - "name": "hmrc/state_pension_income_band_200_000_to_300_000", + "fixture_value": 1250000.0, + "kind": "calibration_drift", + "ledger_value": 1249719.0, + "name": "ons.population.age_30_39_by_region@E12000008", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 24900000000.0, - "name": "hmrc/state_pension_income_band_20_000_to_30_000", + "fixture_value": 724000.0, + "kind": "calibration_drift", + "ledger_value": 723707.0, + "name": "ons.population.age_30_39_by_region@E12000009", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 96000000.0, - "name": "hmrc/state_pension_income_band_300_000_to_500_000", + "fixture_value": 258000.0, + "kind": "calibration_drift", + "ledger_value": 254018.0, + "name": "ons.population.age_30_39_by_region@N92000002", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 12300000000.0, - "name": "hmrc/state_pension_income_band_30_000_to_40_000", + "fixture_value": 717000.0, + "kind": "calibration_drift", + "ledger_value": 718109.0, + "name": "ons.population.age_30_39_by_region@S92000003", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 6020000000.0, - "name": "hmrc/state_pension_income_band_40_000_to_50_000", + "fixture_value": 398000.0, + "kind": "calibration_drift", + "ledger_value": 400341.0, + "name": "ons.population.age_30_39_by_region@W92000004", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 43000000.0, - "name": "hmrc/state_pension_income_band_500_000_to_1_000_000", + "fixture_value": 314000.0, + "kind": "calibration_drift", + "ledger_value": 312822.0, + "name": "ons.population.age_40_49_by_region@E12000001", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 4660000000.0, - "name": "hmrc/state_pension_income_band_50_000_to_70_000", + "fixture_value": 914000.0, + "kind": "calibration_drift", + "ledger_value": 909169.0, + "name": "ons.population.age_40_49_by_region@E12000002", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 1890000000.0, - "name": "hmrc/state_pension_income_band_70_000_to_100_000", + "fixture_value": 668000.0, + "kind": "calibration_drift", + "ledger_value": 662244.0, + "name": "ons.population.age_40_49_by_region@E12000003", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 99200000.0, - "name": "nithc.public_transport_support", + "fixture_value": 605000.0, + "kind": "calibration_drift", + "ledger_value": 600162.0, + "name": "ons.population.age_40_49_by_region@E12000004", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 14200000000.0, - "name": "obr.fuel_duties_cars", + "fixture_value": 739000.0, + "kind": "calibration_drift", + "ledger_value": 733710.0, + "name": "ons.population.age_40_49_by_region@E12000005", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 52505000000.0, - "name": "ons.household_energy_expenditure", + "fixture_value": 829000.0, + "kind": "calibration_drift", + "ledger_value": 822990.0, + "name": "ons.population.age_40_49_by_region@E12000006", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 7497928.0, - "name": "ons.population.age_0_9_by_region", + "fixture_value": 1280000.0, + "kind": "calibration_drift", + "ledger_value": 1264515.0, + "name": "ons.population.age_40_49_by_region@E12000007", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 8161755.0, - "name": "ons.population.age_10_19_by_region", + "fixture_value": 1241000.0, + "kind": "calibration_drift", + "ledger_value": 1233676.0, + "name": "ons.population.age_40_49_by_region@E12000008", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 8525289.0, - "name": "ons.population.age_20_29_by_region", + "fixture_value": 686000.0, + "kind": "calibration_drift", + "ledger_value": 678388.0, + "name": "ons.population.age_40_49_by_region@E12000009", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 9336791.0, - "name": "ons.population.age_30_39_by_region", + "fixture_value": 247000.0, + "kind": "calibration_drift", + "ledger_value": 244462.0, + "name": "ons.population.age_40_49_by_region@N92000002", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 8489468.0, - "name": "ons.population.age_40_49_by_region", + "fixture_value": 673000.0, + "kind": "calibration_drift", + "ledger_value": 666581.0, + "name": "ons.population.age_40_49_by_region@S92000003", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 9148143.0, - "name": "ons.population.age_50_59_by_region", + "fixture_value": 364000.0, + "kind": "calibration_drift", + "ledger_value": 360749.0, + "name": "ons.population.age_40_49_by_region@W92000004", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 7671378.0, - "name": "ons.population.age_60_69_by_region", + "fixture_value": 371000.0, + "kind": "calibration_drift", + "ledger_value": 367103.0, + "name": "ons.population.age_50_59_by_region@E12000001", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 5963774.0, - "name": "ons.population.age_70_79_by_region", + "fixture_value": 1018000.0, + "kind": "calibration_drift", + "ledger_value": 1011356.0, + "name": "ons.population.age_50_59_by_region@E12000002", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 2858964.0, - "name": "ons.population.age_80_89_by_region", + "fixture_value": 751000.0, + "kind": "calibration_drift", + "ledger_value": 747499.0, + "name": "ons.population.age_50_59_by_region@E12000003", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 1742660.0, - "name": "ons.population.female_0_4", + "fixture_value": 686000.0, + "kind": "calibration_drift", + "ledger_value": 684525.0, + "name": "ons.population.age_50_59_by_region@E12000004", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 2025209.0, - "name": "ons.population.female_10_14", + "fixture_value": 805000.0, + "kind": "calibration_drift", + "ledger_value": 804649.0, + "name": "ons.population.age_50_59_by_region@E12000005", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 1947075.0, - "name": "ons.population.female_15_19", + "fixture_value": 873000.0, + "kind": "calibration_drift", + "ledger_value": 873937.0, + "name": "ons.population.age_50_59_by_region@E12000006", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 2008105.0, - "name": "ons.population.female_20_24", + "fixture_value": 1086000.0, + "kind": "calibration_drift", + "ledger_value": 1090827.0, + "name": "ons.population.age_50_59_by_region@E12000007", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 2232408.0, - "name": "ons.population.female_25_29", + "fixture_value": 1296000.0, + "kind": "calibration_drift", + "ledger_value": 1296339.0, + "name": "ons.population.age_50_59_by_region@E12000008", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 2417082.0, - "name": "ons.population.female_30_34", + "fixture_value": 800000.0, + "kind": "calibration_drift", + "ledger_value": 796631.0, + "name": "ons.population.age_50_59_by_region@E12000009", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 2396290.0, - "name": "ons.population.female_35_39", + "fixture_value": 258000.0, + "kind": "calibration_drift", + "ledger_value": 258467.0, + "name": "ons.population.age_50_59_by_region@N92000002", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 2282889.0, - "name": "ons.population.female_40_44", + "fixture_value": 793000.0, + "kind": "calibration_drift", + "ledger_value": 783721.0, + "name": "ons.population.age_50_59_by_region@S92000003", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 2056833.0, - "name": "ons.population.female_45_49", + "fixture_value": 436000.0, + "kind": "calibration_drift", + "ledger_value": 433089.0, + "name": "ons.population.age_50_59_by_region@W92000004", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 2304569.0, - "name": "ons.population.female_50_54", + "fixture_value": 341000.0, + "kind": "calibration_drift", + "ledger_value": 342756.0, + "name": "ons.population.age_60_69_by_region@E12000001", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 2360196.0, - "name": "ons.population.female_55_59", + "fixture_value": 858000.0, + "kind": "calibration_drift", + "ledger_value": 862704.0, + "name": "ons.population.age_60_69_by_region@E12000002", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 1916898.0, - "name": "ons.population.female_5_9", + "fixture_value": 635000.0, + "kind": "calibration_drift", + "ledger_value": 638531.0, + "name": "ons.population.age_60_69_by_region@E12000003", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 2133183.0, - "name": "ons.population.female_60_64", + "fixture_value": 572000.0, + "kind": "calibration_drift", + "ledger_value": 576856.0, + "name": "ons.population.age_60_69_by_region@E12000004", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 1794961.0, - "name": "ons.population.female_65_69", + "fixture_value": 661000.0, + "kind": "calibration_drift", + "ledger_value": 663774.0, + "name": "ons.population.age_60_69_by_region@E12000005", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 1633749.0, - "name": "ons.population.female_70_74", + "fixture_value": 721000.0, + "kind": "calibration_drift", + "ledger_value": 726180.0, + "name": "ons.population.age_60_69_by_region@E12000006", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 1518990.0, - "name": "ons.population.female_75_79", + "fixture_value": 751000.0, + "kind": "calibration_drift", + "ledger_value": 762738.0, + "name": "ons.population.age_60_69_by_region@E12000007", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 985351.0, - "name": "ons.population.female_80_84", + "fixture_value": 1052000.0, + "kind": "calibration_drift", + "ledger_value": 1060436.0, + "name": "ons.population.age_60_69_by_region@E12000008", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 650117.0, - "name": "ons.population.female_85_89", + "fixture_value": 716000.0, + "kind": "calibration_drift", + "ledger_value": 719639.0, + "name": "ons.population.age_60_69_by_region@E12000009", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 408216.0, - "name": "ons.population.female_90_plus", + "fixture_value": 217000.0, + "kind": "calibration_drift", + "ledger_value": 218137.0, + "name": "ons.population.age_60_69_by_region@N92000002", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 1829347.0, - "name": "ons.population.male_0_4", + "fixture_value": 702000.0, + "kind": "calibration_drift", + "ledger_value": 706151.0, + "name": "ons.population.age_60_69_by_region@S92000003", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 2125078.0, - "name": "ons.population.male_10_14", + "fixture_value": 393000.0, + "kind": "calibration_drift", + "ledger_value": 393476.0, + "name": "ons.population.age_60_69_by_region@W92000004", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 2064393.0, - "name": "ons.population.male_15_19", + "fixture_value": 264000.0, + "kind": "calibration_drift", + "ledger_value": 261974.0, + "name": "ons.population.age_70_79_by_region@E12000001", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 2089437.0, - "name": "ons.population.male_20_24", + "fixture_value": 680000.0, + "kind": "calibration_drift", + "ledger_value": 666252.0, + "name": "ons.population.age_70_79_by_region@E12000002", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 2195339.0, - "name": "ons.population.male_25_29", + "fixture_value": 510000.0, + "kind": "calibration_drift", + "ledger_value": 500504.0, + "name": "ons.population.age_70_79_by_region@E12000003", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 2283116.0, - "name": "ons.population.male_30_34", + "fixture_value": 475000.0, + "kind": "calibration_drift", + "ledger_value": 465125.0, + "name": "ons.population.age_70_79_by_region@E12000004", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 2240303.0, - "name": "ons.population.male_35_39", + "fixture_value": 542000.0, + "kind": "calibration_drift", + "ledger_value": 528806.0, + "name": "ons.population.age_70_79_by_region@E12000005", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 2163337.0, - "name": "ons.population.male_40_44", + "fixture_value": 612000.0, + "kind": "calibration_drift", + "ledger_value": 597762.0, + "name": "ons.population.age_70_79_by_region@E12000006", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 1986409.0, - "name": "ons.population.male_45_49", + "fixture_value": 484000.0, + "kind": "calibration_drift", + "ledger_value": 477581.0, + "name": "ons.population.age_70_79_by_region@E12000007", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 2218309.0, - "name": "ons.population.male_50_54", + "fixture_value": 882000.0, + "kind": "calibration_drift", + "ledger_value": 862580.0, + "name": "ons.population.age_70_79_by_region@E12000008", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 2265069.0, - "name": "ons.population.male_55_59", + "fixture_value": 630000.0, + "kind": "calibration_drift", + "ledger_value": 616535.0, + "name": "ons.population.age_70_79_by_region@E12000009", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 2009023.0, - "name": "ons.population.male_5_9", + "fixture_value": 157000.0, + "kind": "calibration_drift", + "ledger_value": 154231.0, + "name": "ons.population.age_70_79_by_region@N92000002", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 2048486.0, - "name": "ons.population.male_60_64", + "fixture_value": 520000.0, + "kind": "calibration_drift", + "ledger_value": 512152.0, + "name": "ons.population.age_70_79_by_region@S92000003", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 1694748.0, - "name": "ons.population.male_65_69", + "fixture_value": 327000.0, + "kind": "calibration_drift", + "ledger_value": 320272.0, + "name": "ons.population.age_70_79_by_region@W92000004", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 1486710.0, - "name": "ons.population.male_70_74", + "fixture_value": 145000.0, + "kind": "calibration_drift", + "ledger_value": 118542.0, + "name": "ons.population.age_80_89_by_region@E12000001", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 1324325.0, - "name": "ons.population.male_75_79", + "fixture_value": 379000.0, + "kind": "calibration_drift", + "ledger_value": 313411.0, + "name": "ons.population.age_80_89_by_region@E12000002", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 777961.0, - "name": "ons.population.male_80_84", + "fixture_value": 287000.0, + "kind": "calibration_drift", + "ledger_value": 235572.0, + "name": "ons.population.age_80_89_by_region@E12000003", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 445535.0, - "name": "ons.population.male_85_89", + "fixture_value": 260000.0, + "kind": "calibration_drift", + "ledger_value": 215547.0, + "name": "ons.population.age_80_89_by_region@E12000004", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 203503.0, - "name": "ons.population.male_90_plus", + "fixture_value": 321000.0, + "kind": "calibration_drift", + "ledger_value": 264102.0, + "name": "ons.population.age_80_89_by_region@E12000005", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 68265209.0, - "name": "ons.population.uk_total", + "fixture_value": 360000.0, + "kind": "calibration_drift", + "ledger_value": 293202.0, + "name": "ons.population.age_80_89_by_region@E12000006", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 23120884840.50285, - "name": "orr.rail_government_support", + "fixture_value": 289000.0, + "kind": "calibration_drift", + "ledger_value": 231678.0, + "name": "ons.population.age_80_89_by_region@E12000007", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 438893000.0, - "name": "scotgov.bus.government_support", + "fixture_value": 531000.0, + "kind": "calibration_drift", + "ledger_value": 429759.0, + "name": "ons.population.age_80_89_by_region@E12000008", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "kind": "ledger_only", - "ledger_value": 97426240.0, - "name": "welshgov.bus.public_support", + "fixture_value": 369000.0, + "kind": "calibration_drift", + "ledger_value": 299993.0, + "name": "ons.population.age_80_89_by_region@E12000009", "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 557000.0, + "fixture_value": 89000.0, "kind": "calibration_drift", - "ledger_value": 554243.0, - "name": "isc.private_school_students", + "ledger_value": 74648.0, + "name": "ons.population.age_80_89_by_region@N92000002", "period": 2023, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 1957550396058.5835, + "fixture_value": 282000.0, "kind": "calibration_drift", - "ledger_value": 1976893000000.0, - "name": "ons.land.corporate_land_value", + "ledger_value": 233342.0, + "name": "ons.population.age_80_89_by_region@S92000003", "period": 2023, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { - "fixture_value": 4798764603941.416, + "fixture_value": 181000.0, "kind": "calibration_drift", - "ledger_value": 4383082000000.0, - "name": "ons.land.household_land_value", + "ledger_value": 149168.0, + "name": "ons.population.age_80_89_by_region@W92000004", "period": 2023, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." + "reason": "Region-tier ONS population cell (microcosm#905): the Ledger row binds the ONS mid-year estimate by single year of age at Chronicle's region or country stamp, held to the comparison period as identity, while the incumbent's regional row is an ONS subnational projection rounded to the nearest thousand. The difference is projection-versus-estimate vintage and rounding, never area or band definition: the nine English regions and three nations sum exactly to the retired UK-wide row of the same publication." }, { "fixture_value": 46000.0, diff --git a/packages/microcosm-build/src/microcosm/build/uk/local_area_crosswalk.json b/packages/microcosm-build/src/microcosm/build/uk/local_area_crosswalk.json index 4661ce3f1..3281bfb8a 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/local_area_crosswalk.json +++ b/packages/microcosm-build/src/microcosm/build/uk/local_area_crosswalk.json @@ -688,7 +688,659 @@ "W07000110", "W07000111", "W07000112" - ] + ], + "region_code_by_area": { + "E14001063": "E12000008", + "E14001064": "E12000005", + "E14001065": "E12000002", + "E14001066": "E12000004", + "E14001067": "E12000008", + "E14001068": "E12000004", + "E14001069": "E12000008", + "E14001070": "E12000002", + "E14001071": "E12000008", + "E14001072": "E12000008", + "E14001073": "E12000007", + "E14001074": "E12000003", + "E14001075": "E12000003", + "E14001076": "E12000002", + "E14001077": "E12000006", + "E14001078": "E12000008", + "E14001079": "E12000004", + "E14001080": "E12000009", + "E14001081": "E12000007", + "E14001082": "E12000008", + "E14001083": "E12000007", + "E14001084": "E12000006", + "E14001085": "E12000007", + "E14001086": "E12000007", + "E14001087": "E12000003", + "E14001088": "E12000008", + "E14001089": "E12000007", + "E14001090": "E12000008", + "E14001091": "E12000002", + "E14001092": "E12000005", + "E14001093": "E12000005", + "E14001094": "E12000005", + "E14001095": "E12000005", + "E14001096": "E12000005", + "E14001097": "E12000005", + "E14001098": "E12000005", + "E14001099": "E12000005", + "E14001100": "E12000005", + "E14001101": "E12000001", + "E14001102": "E12000002", + "E14001103": "E12000002", + "E14001104": "E12000002", + "E14001105": "E12000002", + "E14001106": "E12000001", + "E14001107": "E12000001", + "E14001108": "E12000008", + "E14001109": "E12000004", + "E14001110": "E12000002", + "E14001111": "E12000002", + "E14001112": "E12000002", + "E14001113": "E12000002", + "E14001114": "E12000004", + "E14001115": "E12000009", + "E14001116": "E12000009", + "E14001117": "E12000008", + "E14001118": "E12000003", + "E14001119": "E12000003", + "E14001120": "E12000003", + "E14001121": "E12000006", + "E14001122": "E12000007", + "E14001123": "E12000007", + "E14001124": "E12000007", + "E14001125": "E12000006", + "E14001126": "E12000009", + "E14001127": "E12000003", + "E14001128": "E12000003", + "E14001129": "E12000008", + "E14001130": "E12000008", + "E14001131": "E12000009", + "E14001132": "E12000009", + "E14001133": "E12000009", + "E14001134": "E12000009", + "E14001135": "E12000009", + "E14001136": "E12000006", + "E14001137": "E12000007", + "E14001138": "E12000005", + "E14001139": "E12000006", + "E14001140": "E12000004", + "E14001141": "E12000008", + "E14001142": "E12000002", + "E14001143": "E12000005", + "E14001144": "E12000002", + 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"local_authority": { "ledger_geography_level": "local_authority", @@ -1094,7 +1746,370 @@ "W06000022", "W06000023", "W06000024" - ] + ], + "region_code_by_area": { + "E06000001": "E12000001", + "E06000002": "E12000001", + "E06000003": "E12000001", + "E06000004": "E12000001", + "E06000005": "E12000001", + "E06000006": "E12000002", + "E06000007": "E12000002", + "E06000008": "E12000002", + "E06000009": "E12000002", + "E06000010": "E12000003", + "E06000011": "E12000003", + "E06000012": "E12000003", + "E06000013": "E12000003", + "E06000014": "E12000003", + "E06000015": "E12000004", + "E06000016": "E12000004", + "E06000017": "E12000004", + "E06000018": "E12000004", + "E06000019": "E12000005", + "E06000020": "E12000005", + "E06000021": "E12000005", + "E06000022": "E12000009", + "E06000023": "E12000009", + "E06000024": "E12000009", + "E06000025": "E12000009", + "E06000026": "E12000009", + "E06000027": "E12000009", + "E06000030": "E12000009", + "E06000031": "E12000006", + "E06000032": "E12000006", + "E06000033": "E12000006", + "E06000034": "E12000006", + "E06000035": "E12000008", + "E06000036": "E12000008", + "E06000037": "E12000008", + "E06000038": "E12000008", + "E06000039": "E12000008", + "E06000040": "E12000008", + "E06000041": "E12000008", + "E06000042": "E12000008", + "E06000043": "E12000008", + "E06000044": "E12000008", + "E06000045": "E12000008", + "E06000046": "E12000008", + "E06000047": "E12000001", + "E06000049": "E12000002", + "E06000050": "E12000002", + "E06000051": "E12000005", + "E06000052": "E12000009", + "E06000053": "E12000009", + "E06000054": "E12000009", + "E06000055": "E12000006", + "E06000056": "E12000006", + "E06000057": "E12000001", + "E06000058": "E12000009", + "E06000059": "E12000009", + "E06000060": "E12000008", + "E06000061": "E12000004", + "E06000062": "E12000004", + "E06000063": "E12000002", + "E06000064": "E12000002", + "E06000065": "E12000003", + "E06000066": "E12000009", + "E07000008": "E12000006", + "E07000009": "E12000006", + "E07000010": "E12000006", + "E07000011": "E12000006", + "E07000012": "E12000006", + "E07000032": "E12000004", + "E07000033": "E12000004", + "E07000034": "E12000004", + "E07000035": "E12000004", + "E07000036": "E12000004", + "E07000037": "E12000004", + "E07000038": "E12000004", + "E07000039": "E12000004", + "E07000040": "E12000009", + "E07000041": "E12000009", + "E07000042": "E12000009", + "E07000043": "E12000009", + "E07000044": "E12000009", + "E07000045": "E12000009", + "E07000046": "E12000009", + "E07000047": "E12000009", + "E07000061": "E12000008", + "E07000062": "E12000008", + "E07000063": "E12000008", + "E07000064": "E12000008", + "E07000065": "E12000008", + "E07000066": "E12000006", + "E07000067": "E12000006", + "E07000068": "E12000006", + "E07000069": "E12000006", + "E07000070": "E12000006", + "E07000071": "E12000006", + "E07000072": "E12000006", + "E07000073": "E12000006", + "E07000074": "E12000006", + "E07000075": "E12000006", + "E07000076": "E12000006", + "E07000077": "E12000006", + "E07000078": "E12000009", + "E07000079": "E12000009", + "E07000080": "E12000009", + "E07000081": "E12000009", + "E07000082": "E12000009", + "E07000083": "E12000009", + "E07000084": "E12000008", + "E07000085": "E12000008", + "E07000086": "E12000008", + "E07000087": "E12000008", + "E07000088": "E12000008", + "E07000089": "E12000008", + "E07000090": "E12000008", + "E07000091": "E12000008", + "E07000092": "E12000008", + "E07000093": "E12000008", + "E07000094": "E12000008", + "E07000095": "E12000006", + "E07000096": "E12000006", + "E07000098": "E12000006", + "E07000099": "E12000006", + "E07000102": "E12000006", + "E07000103": "E12000006", + "E07000105": "E12000008", + "E07000106": "E12000008", + "E07000107": "E12000008", + "E07000108": "E12000008", + "E07000109": "E12000008", + "E07000110": "E12000008", + "E07000111": "E12000008", + "E07000112": "E12000008", + "E07000113": "E12000008", + "E07000114": "E12000008", + "E07000115": "E12000008", + "E07000116": "E12000008", + "E07000117": "E12000002", + "E07000118": "E12000002", + "E07000119": "E12000002", + "E07000120": "E12000002", + "E07000121": "E12000002", + "E07000122": "E12000002", + "E07000123": "E12000002", + "E07000124": "E12000002", + "E07000125": "E12000002", + "E07000126": "E12000002", + "E07000127": "E12000002", + "E07000128": "E12000002", + "E07000129": "E12000004", + "E07000130": "E12000004", + "E07000131": "E12000004", + "E07000132": "E12000004", + "E07000133": "E12000004", + "E07000134": "E12000004", + "E07000135": "E12000004", + "E07000136": "E12000004", + "E07000137": "E12000004", + "E07000138": "E12000004", + "E07000139": "E12000004", + "E07000140": "E12000004", + "E07000141": "E12000004", + "E07000142": "E12000004", + "E07000143": "E12000006", + "E07000144": "E12000006", + "E07000145": "E12000006", + "E07000146": "E12000006", + "E07000147": "E12000006", + "E07000148": "E12000006", + "E07000149": "E12000006", + "E07000170": "E12000004", + "E07000171": "E12000004", + "E07000172": "E12000004", + "E07000173": "E12000004", + "E07000174": "E12000004", + "E07000175": "E12000004", + "E07000176": "E12000004", + "E07000177": "E12000008", + "E07000178": "E12000008", + "E07000179": "E12000008", + "E07000180": "E12000008", + "E07000181": "E12000008", + "E07000192": "E12000005", + "E07000193": "E12000005", + "E07000194": "E12000005", + "E07000195": "E12000005", + "E07000196": "E12000005", + "E07000197": "E12000005", + "E07000198": "E12000005", + "E07000199": "E12000005", + "E07000200": "E12000006", + "E07000202": "E12000006", + "E07000203": "E12000006", + "E07000207": "E12000008", + "E07000208": "E12000008", + "E07000209": "E12000008", + "E07000210": "E12000008", + "E07000211": "E12000008", + "E07000212": "E12000008", + "E07000213": "E12000008", + "E07000214": "E12000008", + "E07000215": "E12000008", + "E07000216": "E12000008", + "E07000217": "E12000008", + "E07000218": "E12000005", + "E07000219": "E12000005", + "E07000220": "E12000005", + "E07000221": "E12000005", + "E07000222": "E12000005", + "E07000223": "E12000008", + "E07000224": "E12000008", + "E07000225": "E12000008", + "E07000226": "E12000008", + "E07000227": "E12000008", + "E07000228": "E12000008", + "E07000229": "E12000008", + "E07000234": "E12000005", + "E07000235": "E12000005", + "E07000236": "E12000005", + "E07000237": "E12000005", + "E07000238": "E12000005", + "E07000239": "E12000005", + "E07000240": "E12000006", + "E07000241": "E12000006", + "E07000242": "E12000006", + "E07000243": "E12000006", + "E07000244": "E12000006", + "E07000245": "E12000006", + "E08000001": "E12000002", + "E08000002": "E12000002", + "E08000003": "E12000002", + "E08000004": "E12000002", + "E08000005": "E12000002", + "E08000006": "E12000002", + "E08000007": "E12000002", + "E08000008": "E12000002", + "E08000009": "E12000002", + "E08000010": "E12000002", + "E08000011": "E12000002", + "E08000012": "E12000002", + "E08000013": "E12000002", + "E08000014": "E12000002", + "E08000015": "E12000002", + "E08000016": "E12000003", + "E08000017": "E12000003", + "E08000018": "E12000003", + "E08000019": "E12000003", + "E08000021": "E12000001", + "E08000022": "E12000001", + "E08000023": "E12000001", + "E08000024": "E12000001", + "E08000025": "E12000005", + "E08000026": "E12000005", + "E08000027": "E12000005", + "E08000028": "E12000005", + "E08000029": "E12000005", + "E08000030": "E12000005", + "E08000031": "E12000005", + "E08000032": "E12000003", + "E08000033": "E12000003", + "E08000034": "E12000003", + "E08000035": "E12000003", + "E08000036": "E12000003", + "E08000037": "E12000001", + "E09000001": "E12000007", + "E09000002": "E12000007", + "E09000003": "E12000007", + "E09000004": "E12000007", + "E09000005": "E12000007", + "E09000006": "E12000007", + "E09000007": "E12000007", + "E09000008": "E12000007", + "E09000009": "E12000007", + "E09000010": "E12000007", + "E09000011": "E12000007", + "E09000012": "E12000007", + "E09000013": "E12000007", + "E09000014": "E12000007", + "E09000015": "E12000007", + "E09000016": "E12000007", + "E09000017": "E12000007", + "E09000018": "E12000007", + "E09000019": "E12000007", + "E09000020": "E12000007", + "E09000021": "E12000007", + "E09000022": "E12000007", + "E09000023": "E12000007", + "E09000024": "E12000007", + "E09000025": "E12000007", + "E09000026": "E12000007", + "E09000027": "E12000007", + "E09000028": "E12000007", + "E09000029": "E12000007", + "E09000030": "E12000007", + "E09000031": "E12000007", + "E09000032": "E12000007", + "E09000033": "E12000007", + "N09000001": "N92000002", + "N09000002": "N92000002", + "N09000003": "N92000002", + "N09000004": "N92000002", + "N09000005": "N92000002", + "N09000006": "N92000002", + "N09000007": "N92000002", + "N09000008": "N92000002", + "N09000009": "N92000002", + "N09000010": "N92000002", + "N09000011": "N92000002", + "S12000005": "S92000003", + "S12000006": "S92000003", + "S12000008": "S92000003", + "S12000010": "S92000003", + "S12000011": "S92000003", + "S12000013": "S92000003", + "S12000014": "S92000003", + "S12000017": "S92000003", + "S12000018": "S92000003", + "S12000019": "S92000003", + "S12000020": "S92000003", + "S12000021": "S92000003", + "S12000023": "S92000003", + "S12000026": "S92000003", + "S12000027": "S92000003", + "S12000028": "S92000003", + "S12000029": "S92000003", + "S12000030": "S92000003", + "S12000033": "S92000003", + "S12000034": "S92000003", + "S12000035": "S92000003", + "S12000036": "S92000003", + "S12000038": "S92000003", + "S12000039": "S92000003", + "S12000040": "S92000003", + "S12000041": "S92000003", + "S12000042": "S92000003", + "S12000045": "S92000003", + "S12000047": "S92000003", + "S12000048": "S92000003", + "S12000049": "S92000003", + "S12000050": "S92000003", + "W06000001": "W92000004", + "W06000002": "W92000004", + "W06000003": "W92000004", + "W06000004": "W92000004", + "W06000005": "W92000004", + "W06000006": "W92000004", + "W06000008": "W92000004", + "W06000009": "W92000004", + "W06000010": "W92000004", + "W06000011": "W92000004", + "W06000012": "W92000004", + "W06000013": "W92000004", + "W06000014": "W92000004", + "W06000015": "W92000004", + "W06000016": "W92000004", + "W06000018": "W92000004", + "W06000019": "W92000004", + "W06000020": "W92000004", + "W06000021": "W92000004", + "W06000022": "W92000004", + "W06000023": "W92000004", + "W06000024": "W92000004" + } } } } diff --git a/packages/microcosm-build/src/microcosm/build/uk/uk_data_target_parity.json b/packages/microcosm-build/src/microcosm/build/uk/uk_data_target_parity.json index aecca0f0a..f1355e929 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/uk_data_target_parity.json +++ b/packages/microcosm-build/src/microcosm/build/uk/uk_data_target_parity.json @@ -159,19 +159,14 @@ "status": "ported_local_declared" }, { - "classification": "cross_grain_grain_gap", + "classification": "cross_grain_rule", "concern_id": "cross_grain_council_tax_stock_england_region", "covers": [ "policyengine_uk_data.targets.sources.voa_council_tax" ], - "evidence": "Microcosm voa.council_tax_stock.band_* geography_levels include region; CrossGrainRule.grain_precedence is country,constituency,la.", - "fence": { - "origin": "The national contract materializes English VOA stock by region and Wales by country.", - "purpose": "Prevent the LA solve from implicitly choosing between conflicting regional and local stock totals.", - "verdict_basis": "Use exact-signature country reconciliation where a country leg exists; record the England regional leg here until #802 machinery gains an adjudicated region bridge." - }, - "reason": "The incumbent English VOA controls are region-grain, while the #802 rule declares only country > constituency > LA and cannot accept region rows.", - "status": "routed" + "evidence": "voa.council_tax_stock.band_* references voa.council_tax_stock.band_*@E12000001..E12000009; UK_CROSS_GRAIN_RULE.grain_precedence includes region and parent_geography_legs maps every E12 code to itself; the local crosswalk carries region_code_by_area from the ladder.", + "reason": "The English VOA stock rows fan out over the nine English regions and sit at the region grain of the cross-grain rule (country > region > constituency > la); each region is its own leg, so the LA solve reconciles to its region's published stock rather than to a single England row (microcosm#905).", + "status": "ported_national" }, { "classification": "cross_grain_grain_gap", diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/battery_bindings.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/battery_bindings.py index f4e907507..2511ad4ce 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/battery_bindings.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/battery_bindings.py @@ -63,6 +63,7 @@ from microcosm.build.uk_runtime.ledger_targets import ( LOCAL_REGISTRY_PARITY_FIXTURE_RESOURCE, align_uk_local_registry_parity_fixture, + align_uk_national_registry_parity_fixture, ) from microcosm.build.uk_runtime.local_targets import ( load_uk_local_geography_contract, @@ -1256,7 +1257,9 @@ def _load_ledger_compile_parity_fixture(fixture_resource: str) -> dict[str, Any] ) if fixture_resource == LOCAL_REGISTRY_PARITY_FIXTURE_RESOURCE: return align_uk_local_registry_parity_fixture(fixture) - return fixture + # The national fixtures spell the incumbent's regional rows their own way; + # the receipts are signed against the region-tier names (microcosm#905). + return align_uk_national_registry_parity_fixture(fixture) def _ledger_compile_parity_evidence( diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/data_target_parity.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/data_target_parity.py index 96d22d57a..475793538 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/data_target_parity.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/data_target_parity.py @@ -392,15 +392,10 @@ def _fence(origin: str, purpose: str, verdict_basis: str) -> dict[str, str]: }, { "concern_id": "cross_grain_council_tax_stock_england_region", - "status": "routed", - "classification": "cross_grain_grain_gap", - "reason": "The incumbent English VOA controls are region-grain, while the #802 rule declares only country > constituency > LA and cannot accept region rows.", - "evidence": "Microcosm voa.council_tax_stock.band_* geography_levels include region; CrossGrainRule.grain_precedence is country,constituency,la.", - "fence": _fence( - "The national contract materializes English VOA stock by region and Wales by country.", - "Prevent the LA solve from implicitly choosing between conflicting regional and local stock totals.", - "Use exact-signature country reconciliation where a country leg exists; record the England regional leg here until #802 machinery gains an adjudicated region bridge.", - ), + "status": "ported_national", + "classification": "cross_grain_rule", + "reason": "The English VOA stock rows fan out over the nine English regions and sit at the region grain of the cross-grain rule (country > region > constituency > la); each region is its own leg, so the LA solve reconciles to its region's published stock rather than to a single England row (microcosm#905).", + "evidence": "voa.council_tax_stock.band_* references voa.council_tax_stock.band_*@E12000001..E12000009; UK_CROSS_GRAIN_RULE.grain_precedence includes region and parent_geography_legs maps every E12 code to itself; the local crosswalk carries region_code_by_area from the ladder.", }, { "concern_id": "local_council_tax_band_d_rate", diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/ledger_targets.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/ledger_targets.py index 6b325b40e..c9f73bf80 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/ledger_targets.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/ledger_targets.py @@ -29,11 +29,14 @@ materialize_target_bindings, ) from microcosm.build.uk_runtime.cgt_calibration import uk_cgt_annual_exempt_amount -from microcosm.build.uk_runtime.geography_ladder import UK_ENGLAND_WALES_REGION_CODES +from microcosm.build.uk_runtime.geography_ladder import ( + UK_ENGLAND_WALES_REGION_CODES, + UK_REGION_TIER, + UK_REGION_TIER_ENUM, +) from microcosm.build.uk_runtime.local_target_census import family_for_metric from microcosm.build.uk_runtime.local_targets import ( AREA_TYPE_TO_LEDGER_GEOGRAPHY_LEVEL, - area_groups_from_codes, load_uk_local_geography_contract, metric_names, ) @@ -62,20 +65,84 @@ class UKLedgerTargetCompilation: UK_CENSUS_HOUSEHOLDS_TARGET_ID = "ons.census.households" +#: Every region-tier code is a leg of its own; the national parents cover the +#: tier members they contain (microcosm#905). +UK_REGION_TIER_CODES: tuple[str, ...] = tuple(code for _, code in UK_REGION_TIER) +_UK_ENGLISH_REGION_CODES: tuple[str, ...] = tuple( + code for level, code in UK_REGION_TIER if level == "region" +) +_UK_NATION_LEG_BY_PREFIX = { + "W": "W92000004", + "S": "S92000003", + "N": "N92000002", +} +_UK_CROSSWALK_REGION_BY_AREA: dict[str, str] | None = None + + +def _uk_crosswalk_region_by_area() -> dict[str, str]: + """Area id -> region-tier code from the committed local-area crosswalk.""" + + global _UK_CROSSWALK_REGION_BY_AREA + if _UK_CROSSWALK_REGION_BY_AREA is None: + mapping: dict[str, str] = {} + levels = load_uk_local_area_crosswalk().get("levels") or {} + for level, payload in levels.items(): + by_area = payload.get("region_code_by_area") if payload else None + if not isinstance(by_area, Mapping) or not by_area: + raise ValueError( + f"UK local area crosswalk level {level!r} carries no " + "region_code_by_area; regenerate it from the ladder " + "(tools/generate_uk_local_area_crosswalk.py)." + ) + for area_id, region in by_area.items(): + previous = mapping.setdefault(str(area_id), str(region)) + if previous != str(region): + raise ValueError( + f"UK local area crosswalk maps {area_id!r} to both " + f"{previous!r} and {region!r}." + ) + _UK_CROSSWALK_REGION_BY_AREA = mapping + return _UK_CROSSWALK_REGION_BY_AREA + + def _uk_cross_grain_leg_of_area(area_code: str) -> str: - # area_groups_from_codes maps code -> country group, so the single value is - # this code's leg. It refuses an unknown prefix itself; the explicit miss - # below keeps the refusal fail-closed rather than a bare StopIteration if - # that mapping ever returns nothing for a code. - leg = next(iter(area_groups_from_codes((area_code,)).values()), "") - if not leg: + """The region-tier leg an area belongs to (microcosm#905). + + A region-tier code is its own leg. A Welsh, Scottish or Northern Irish + area maps to its nation by GSS prefix: the tier does not subdivide the + nations. An English constituency or authority resolves through the + crosswalk's ladder-derived region membership, and an English code the + crosswalk does not carry refuses rather than falling back to an + ``England`` leg that no control covers. + """ + + code = str(area_code).strip() + if not code: + raise ValueError("UK cross-grain area code must not be blank.") + if code in UK_REGION_TIER_CODES: + return code + prefix = code[0].upper() + nation = _UK_NATION_LEG_BY_PREFIX.get(prefix) + if nation is not None: + return nation + if prefix != "E": + raise ValueError(f"Unknown UK area code prefix {prefix!r} for code {code!r}.") + region = _uk_crosswalk_region_by_area().get(code) + if region is None: raise ValueError( - f"UK cross-grain area code {area_code!r} maps to no country leg." + f"UK cross-grain area code {code!r} is not in the local-area " + "crosswalk, so it has no region-tier leg." ) - return leg + return region -UK_CROSS_GRAIN_GRAIN_PRECEDENCE = ("country", "constituency", "la") +UK_CROSS_GRAIN_GRAIN_PRECEDENCE = ("country", "region", "constituency", "la") +UK_CROSS_GRAIN_PARENT_GEOGRAPHY_LEGS: dict[str, tuple[str, ...]] = { + **{code: (code,) for code in UK_REGION_TIER_CODES}, + "K02000001": UK_REGION_TIER_CODES, + "K03000001": (*_UK_ENGLISH_REGION_CODES, "W92000004", "S92000003"), + "E92000001": _UK_ENGLISH_REGION_CODES, +} UK_CROSS_GRAIN_BRIDGES = ( CrossGrainBridge( bridge_id="national_household_composition_partition_vs_census_households", @@ -100,11 +167,11 @@ def _uk_cross_grain_leg_of_area(area_code: str) -> str: higher_target_ids=("dwp.uc.households",), lower_side="contract:dwp.uc.households_by_area", ), - # The national ONS controls use inclusive integer-age bands (0--9), while - # local targets use equivalent half-open encodings (0--10), so their - # signatures cannot match directly. These bridges let the K02000001 UK - # control rescale both constituency and local-authority bands over its - # England/Wales/Scotland/Northern Ireland legs. + # The ONS controls use inclusive integer-age bands (0--9), while local + # targets use equivalent half-open encodings (0--10), so their signatures + # cannot match directly. These bridges let the region-tier controls (one + # row per English region and per nation, microcosm#905) rescale both + # constituency and local-authority bands over their twelve legs. CrossGrainBridge( bridge_id="national_age_0_9_vs_local_age_0_10", concept="uk.person.count", @@ -161,17 +228,85 @@ def _uk_cross_grain_leg_of_area(area_code: str) -> str: signature_fields=("concept", "entity", "map_to", "filters"), bridges=UK_CROSS_GRAIN_BRIDGES, leg_of_area=_uk_cross_grain_leg_of_area, - parent_geography_legs={ - "K02000001": ("England", "Wales", "Scotland", "Northern Ireland"), - "K03000001": ("England", "Wales", "Scotland"), - "E92000001": ("England",), - "W92000004": ("Wales",), - "S92000003": ("Scotland",), - "N92000002": ("Northern Ireland",), - }, + parent_geography_legs=UK_CROSS_GRAIN_PARENT_GEOGRAPHY_LEGS, ) +#: The incumbent's regional row spellings: ``ons/_age__`` uses +#: these slugs, ``voa/council_tax//`` the spine's enum names. +_UK_INCUMBENT_REGION_SLUG_CODES: dict[str, str] = { + "north_east": "E12000001", + "north_west": "E12000002", + "yorkshire_and_the_humber": "E12000003", + "east_midlands": "E12000004", + "west_midlands": "E12000005", + "east": "E12000006", + "london": "E12000007", + "south_east": "E12000008", + "south_west": "E12000009", + "wales": "W92000004", + "scotland": "S92000003", + "northern_ireland": "N92000002", +} +_UK_INCUMBENT_REGION_ENUM_CODES: dict[str, str] = { + enum: code for code, enum in UK_REGION_TIER_ENUM.items() +} + + +def align_uk_national_registry_parity_fixture( + fixture: Mapping[str, Any], +) -> dict[str, Any]: + """Align the incumbent's regional rows to the region-tier reference names. + + The incumbent measures ``ons/london_age_0_9`` and + ``voa/council_tax/LONDON/A``; the region tier names the same cells + ``ons.population.age_0_9_by_region@E12000007`` and + ``voa.council_tax_stock.band_a@E12000007`` (microcosm#905). Renaming the + fixture rows lets the compile-parity receipt compare values instead of + recording 189 ``fixture_only`` rows against 189 ``ledger_only`` rows. + Rows that are not regional cells pass through untouched. + """ + + rows: list[dict[str, Any]] = [] + for row in fixture.get("rows", ()): + if not isinstance(row, Mapping): + rows.append(row) + continue + updated = dict(row) + aligned = _uk_region_tier_fixture_name(str(updated.get("name") or "")) + if aligned is not None: + updated["name"] = aligned + updated["measure"] = aligned + updated["contract_target_id"] = aligned.split("@", 1)[0] + rows.append(updated) + result = dict(fixture) + result["rows"] = rows + return result + + +def _uk_region_tier_fixture_name(name: str) -> str | None: + if name.startswith("ons/") and "_age_" in name: + slug, _, band = name.removeprefix("ons/").partition("_age_") + code = _UK_INCUMBENT_REGION_SLUG_CODES.get(slug) + lower, _, upper = band.partition("_") + if code is None or not (lower.isdigit() and upper.isdigit()): + return None + return f"ons.population.age_{lower}_{upper}_by_region@{code}" + if name.startswith("voa/council_tax/"): + parts = name.removeprefix("voa/council_tax/").split("/") + if len(parts) != 2: + return None + region, band = parts + code = _UK_INCUMBENT_REGION_ENUM_CODES.get(region) + if code is None or not code.startswith("E12"): + return None + if band == "total": + return f"voa.council_tax_stock.total@{code}" + if band in tuple("ABCDEFGH"): + return f"voa.council_tax_stock.band_{band.lower()}@{code}" + return None + + def align_uk_local_registry_parity_fixture( fixture: Mapping[str, Any], ) -> dict[str, Any]: @@ -1626,9 +1761,22 @@ def uk_local_target_surface( f"UK national target cell {spec.name!r} has non-finite " f"value {value!r}." ) + # A region-tier row declares the grain the cross-grain operator + # places it at (``cross_grain_grain``): Chronicle stamps the + # Welsh, Scottish and Northern Irish population at ``country``, + # but on the surface those rows are the same ITL1 tier as the + # nine English regions and must not outrank them (microcosm#905). + # The selector and ledger metadata keep Chronicle's level. + grain = str(spec.metadata.get("cross_grain_grain") or geography_level) + if grain not in UK_CROSS_GRAIN_GRAIN_PRECEDENCE: + raise ValueError( + f"UK national target cell {spec.name!r} declares " + f"cross_grain_grain {grain!r}, which is not one of " + f"{UK_CROSS_GRAIN_GRAIN_PRECEDENCE}." + ) national_control_groups.setdefault( (contract_target_id, geography_id), [] - ).append((spec.name, geography_level, value)) + ).append((spec.name, grain, value)) else: raise ValueError( f"UK target {spec.name!r} names unsupported geography_level " diff --git a/packages/microcosm-build/tests/test_uk_ledger_targets.py b/packages/microcosm-build/tests/test_uk_ledger_targets.py index 737909109..688d4b6fa 100644 --- a/packages/microcosm-build/tests/test_uk_ledger_targets.py +++ b/packages/microcosm-build/tests/test_uk_ledger_targets.py @@ -10,11 +10,15 @@ from microcosm.build.uk_runtime.ledger_targets import ( UK_CROSS_GRAIN_BRIDGES, UK_CROSS_GRAIN_GRAIN_PRECEDENCE, + UK_CROSS_GRAIN_PARENT_GEOGRAPHY_LEGS, UK_CROSS_GRAIN_RULE, + UK_REGION_TIER_CODES, _spec_geography, _uk_contract_targets, + _uk_cross_grain_leg_of_area, _uk_licensed_empty_legs_from_membership, align_uk_local_registry_parity_fixture, + align_uk_national_registry_parity_fixture, apply_uk_cross_grain_reconciliation, compile_uk_local_target_registry, compile_uk_target_registry, @@ -49,7 +53,7 @@ def test_uk_local_target_surface_uses_registry_names_and_reconciles() -> None: }, ), TargetSpec( - name="dwp.uc.households_by_area@E14000001", + name="dwp.uc.households_by_area@E14001073", entity="household", value=30.0, measure="uc_households", @@ -59,7 +63,7 @@ def test_uk_local_target_surface_uses_registry_names_and_reconciles() -> None: metadata={ "contract_target_id": "dwp.uc.households_by_area", "geography_level": "constituency", - "geography_id": "E14000001", + "geography_id": "E14001073", }, ), TargetSpec( @@ -88,7 +92,7 @@ def test_uk_local_target_surface_uses_registry_names_and_reconciles() -> None: uc = surface.loc[surface["metric"] == "uc_households"] assert uc["value"].tolist() == [60.0, 30.0] assert uc["target_name"].tolist() == [ - "dwp.uc.households_by_area@E14000001", + "dwp.uc.households_by_area@E14001073", "dwp.uc.households_by_area@S14000001", ] contract = _uk_contract_targets(national_only=False) @@ -126,9 +130,9 @@ def test_uk_local_target_surface_fires_k020_household_partition_bridge() -> None for position, target_id in enumerate(composition_ids) ] + [ - _household_spec("E14000001", "constituency", 10.0), + _household_spec("E14001073", "constituency", 10.0), _household_spec("S14000001", "constituency", 20.0), - _household_spec("E06000001", "local_authority", 10.0), + _household_spec("E09000001", "local_authority", 10.0), _household_spec("S12000005", "local_authority", 20.0), ], country="uk", @@ -244,7 +248,7 @@ def test_uk_local_target_surface_excludes_fanout_from_cross_grain_controls() -> "geography_id": area, }, ) - for area, value in (("E14000001", 30.0), ("S14000001", 15.0)) + for area, value in (("E14001073", 30.0), ("S14000001", 15.0)) ] ) @@ -371,7 +375,7 @@ def test_uk_local_target_surface_refuses_fanout_across_levels() -> None: ( { "ledger_geography_level": "constituency", - "ledger_geography_id": "E14000001", + "ledger_geography_id": "E14001073", }, "dwp.uc.households.*contract.*country", ), @@ -1025,11 +1029,13 @@ def test_household_condition_still_reduces_group_entities(): def test_uk_cross_grain_rule_constants_are_review_pinned(): assert UK_CROSS_GRAIN_GRAIN_PRECEDENCE == ( "country", + "region", "constituency", "la", ) assert UK_CROSS_GRAIN_RULE.grain_precedence == ( "country", + "region", "constituency", "la", ) @@ -1086,19 +1092,50 @@ def test_uk_age_cross_grain_bridges_are_complete_and_exclude_80_89(): ) -def test_uk_age_bridge_rescales_constituency_and_la_to_uk_control(): +def test_uk_age_bridge_rescales_constituency_and_la_to_region_controls(): + """Region-tier rows are the controls; each leg is one region or nation. + + London and the North East stand for the nine English regions (whose + constituencies and authorities resolve their leg through the crosswalk's + ladder-derived membership); Wales, Scotland and Northern Ireland are + Chronicle ``country`` rows that the surface places at the region grain + (microcosm#905), so here they carry ``grain: region`` directly. + """ + national_id = "ons.population.age_0_9_by_region" local_id = "ons.age.0_10" - constituency_values = (25.0, 25.0, 25.0, 24.999) - la_values = (25.0, 25.0, 25.0, 25.001) + controls = { + "E12000007": 100.0, + "E12000001": 50.0, + "W92000004": 40.0, + "S92000003": 30.0, + "N92000002": 20.0, + } + constituencies = { + "E14001073": 60.0, # London + "E14001101": 20.0, # North East + "W07000041": 30.0, + "S14000001": 25.0, + "N06000001": 15.0, + } + authorities = { + "E09000001": 80.0, # London + "E06000001": 60.0, # North East + "W06000001": 50.0, + "S12000005": 20.0, + "N09000001": 10.0, + } surface = pd.DataFrame( [ - { - "grain": "country", - "geography_id": "K02000001", - "target_id": national_id, - "value": 100.0, - }, + *[ + { + "grain": "region", + "geography_id": code, + "target_id": national_id, + "value": value, + } + for code, value in controls.items() + ], *[ { "grain": "constituency", @@ -1106,11 +1143,7 @@ def test_uk_age_bridge_rescales_constituency_and_la_to_uk_control(): "target_id": local_id, "value": value, } - for area, value in zip( - ("E14000001", "W07000041", "S14000001", "N06000001"), - constituency_values, - strict=True, - ) + for area, value in constituencies.items() ], *[ { @@ -1119,25 +1152,25 @@ def test_uk_age_bridge_rescales_constituency_and_la_to_uk_control(): "target_id": local_id, "value": value, } - for area, value in zip( - ("E06000001", "W06000001", "S12000005", "N09000001"), - la_values, - strict=True, - ) + for area, value in authorities.items() ], ] ) - reconciled, receipt = apply_uk_cross_grain_reconciliation( surface, (national_id,), ) - - assert reconciled.loc[reconciled["grain"] == "constituency", "value"].sum() == ( - pytest.approx(100.0) + expected_controls = list(controls.values()) + assert reconciled.loc[reconciled["grain"] == "region", "value"].tolist() == ( + expected_controls ) - assert reconciled.loc[reconciled["grain"] == "la", "value"].sum() == pytest.approx( - 100.0 + # One lower row per leg at each grain, so each is rescaled onto its own + # region's control, never onto a UK or England total. + assert reconciled.loc[ + reconciled["grain"] == "constituency", "value" + ].tolist() == pytest.approx(expected_controls) + assert reconciled.loc[reconciled["grain"] == "la", "value"].tolist() == ( + pytest.approx(expected_controls) ) groups = [ group @@ -1145,23 +1178,26 @@ def test_uk_age_bridge_rescales_constituency_and_la_to_uk_control(): if group["bridge_id"] == "national_age_0_9_vs_local_age_0_10" ] assert {group["legs"][0]["reason"] for group in groups} == { - "standing cross-grain rule: country controls constituency", - "standing cross-grain rule: country controls la", + "standing cross-grain rule: region controls constituency", + "standing cross-grain rule: region controls la", } - assert all(group["winning_grain"] == "country" for group in groups) + assert all(group["winning_grain"] == "region" for group in groups) for group in groups: - assert len(group["legs"]) == 1 - leg = group["legs"][0] - assert leg["parent_geography_id"] == "K02000001" - assert leg["leg"] == "England+Wales+Scotland+Northern Ireland" - assert leg["new_total"] == pytest.approx(100.0) - assert leg["declared_factor"] == pytest.approx(1.0, abs=2e-5) + assert [leg["leg"] for leg in group["legs"]] == sorted(controls) + for leg in group["legs"]: + assert leg["parent_geography_id"] == leg["leg"] + assert leg["new_total"] == pytest.approx(controls[leg["leg"]]) + london = next( + leg for leg in groups[0]["legs"] if leg["parent_geography_id"] == "E12000007" + ) + assert london["old_total"] in {60.0, 80.0} + assert london["declared_factor"] == pytest.approx(100.0 / london["old_total"]) def test_uk_empty_leg_licences_require_complete_signed_deferral_coverage(): membership = { "areas_by_geography_level": { - "local_authority": ["E06000001", "S12000005", "S12000006"], + "local_authority": ["E09000001", "S12000005", "S12000006"], }, "signed_deferrals": [ { @@ -1178,7 +1214,7 @@ def test_uk_empty_leg_licences_require_complete_signed_deferral_coverage(): } assert _uk_licensed_empty_legs_from_membership(membership) == { - "voa.council_tax_stock.by_area.band_a": frozenset({"Scotland"}), + "voa.council_tax_stock.by_area.band_a": frozenset({"S92000003"}), } @@ -1202,7 +1238,7 @@ def test_committed_contract_detects_exact_uc_payment_partition(): ], { "grain": "constituency", - "geography_id": "E14000001", + "geography_id": "E14001073", "target_id": "dwp.uc.households_by_area", "value": 30.0, }, @@ -1274,7 +1310,7 @@ def test_real_uk_bridges_resolve_contract_lower_sides(): ], { "grain": "constituency", - "geography_id": "E14000001", + "geography_id": "E14001073", "target_id": "ons.census.households", "value": 40.0, }, @@ -1304,7 +1340,7 @@ def test_real_uk_bridges_resolve_contract_lower_sides(): }, { "grain": "constituency", - "geography_id": "E14000001", + "geography_id": "E14001073", "target_id": "dwp.uc.households_by_area", "value": 30.0, }, @@ -1348,7 +1384,7 @@ def test_reviewed_household_composition_gap_leaves_census_contract_unbound(): ], { "grain": "constituency", - "geography_id": "E14000001", + "geography_id": "E14001073", "target_id": "ons.census.households", "value": 40.0, }, @@ -1405,7 +1441,7 @@ def test_uk_front_door_reconciles_per_country_legs_and_builds_uniform_surface(): }, { "grain": "constituency", - "geography_id": "E14000001", + "geography_id": "E14001073", "target_id": "dwp.uc.households_by_area", "value": 30.0, }, @@ -1427,7 +1463,7 @@ def test_uk_front_door_reconciles_per_country_legs_and_builds_uniform_surface(): index=pd.Index([1, 2], name="household_id"), ) assigned = pd.Series( - ["E14000001", "S14000001"], + ["E14001073", "S14000001"], index=metrics.index, ) @@ -1567,13 +1603,13 @@ def _census_household_registry(*extra: TargetSpec) -> TargetRegistry: return TargetRegistry( [ _household_spec( - "E14000001", "constituency", 10.0, from_period=2021, to_period=2025 + "E14001073", "constituency", 10.0, from_period=2021, to_period=2025 ), _household_spec( "S14000001", "constituency", 20.0, from_period=2022, to_period=2025 ), _household_spec( - "E06000001", "local_authority", 12.0, from_period=2021, to_period=2025 + "E09000001", "local_authority", 12.0, from_period=2021, to_period=2025 ), _household_spec( "S12000005", "local_authority", 18.0, from_period=2022, to_period=2025 @@ -1642,7 +1678,7 @@ def test_uk_local_target_surface_uprates_households_and_tenure_by_grain() -> Non } registry = _census_household_registry( composition_spec, - _tenure_spec("E06000001", 5.0, from_period=2021, to_period=2025), + _tenure_spec("E09000001", 5.0, from_period=2021, to_period=2025), _tenure_spec("S12000005", 4.0, from_period=2022, to_period=2025), _tenure_spec("E06000002", 3.0), ) @@ -1693,7 +1729,7 @@ def test_uk_local_target_surface_refuses_ineligible_household_denominator() -> N uprating = uk_census_household_uprating(registry, reference, period=2025) bad_specs = list(registry.specs) bad_specs[0] = _household_spec( - "E14000001", "constituency", 10.0, from_period=2020, to_period=2025 + "E14001073", "constituency", 10.0, from_period=2020, to_period=2025 ) with pytest.raises(ValueError, match="denominator.*not eligible"): uk_local_target_surface( @@ -1709,7 +1745,7 @@ def _private_rent_target_frame() -> pd.DataFrame: [ { "area_type": "la", - "area_code": "E06000001", + "area_code": "E09000001", "metric": "rent/private_rent", "family": "private_rent", "value": 1_000.0, @@ -1717,7 +1753,7 @@ def _private_rent_target_frame() -> pd.DataFrame: }, { "area_type": "la", - "area_code": "E06000001", + "area_code": "E09000001", "metric": "tenure/private_rent", "family": "tenure", "value": 20.0, @@ -1782,7 +1818,7 @@ def test_uk_private_rent_mean_to_total_composes_and_receipts_cells() -> None: "renter_count_source": "ons.tenure.private_rent (A17-uprated)", "cells_detail": [ { - "area_code": "E06000001", + "area_code": "E09000001", "mean_monthly_rent": 1_000.0, "renter_households": 20.0, "total": 240_000.0, @@ -1820,10 +1856,10 @@ def test_uk_private_rent_mean_to_total_refuses_invalid_inputs_by_area( ) -> None: frame = _private_rent_target_frame() frame.loc[ - (frame["area_code"] == "E06000001") & (frame["metric"] == metric), "value" + (frame["area_code"] == "E09000001") & (frame["metric"] == metric), "value" ] = value - with pytest.raises(ValueError, match="E06000001"): + with pytest.raises(ValueError, match="E09000001"): uk_private_rent_mean_to_total(frame) @@ -1854,7 +1890,7 @@ def spec(name, metric, value): metadata={ "contract_target_id": name.split("@", 1)[0], "geography_level": "local_authority", - "geography_id": "E06000001", + "geography_id": "E09000001", "uprating_from_period": 2021, "uprating_to_period": 2025, }, @@ -1916,7 +1952,7 @@ def test_tenure_receipt_counts_attempted_and_skipped_holds( from_period, to_period, reason, eligible ): registry = _census_household_registry( - _tenure_spec("E06000001", 5.0, from_period=from_period, to_period=to_period) + _tenure_spec("E09000001", 5.0, from_period=from_period, to_period=to_period) ) reference = uk_ledger_households_total( (_households_total_fact(2025, 33.0),), period=2025 @@ -1939,3 +1975,192 @@ def test_tenure_receipt_counts_attempted_and_skipped_holds( assert surface.loc[ surface["metric"] == "tenure/social_rent", "value" ].tolist() == pytest.approx([5.5 if eligible else 5.0]) + + +def test_uk_cross_grain_legs_are_the_region_tier() -> None: + codes = list(UK_REGION_TIER_CODES) + assert len(codes) == 12 + english = [code for code in codes if code.startswith("E12")] + assert len(english) == 9 + legs = UK_CROSS_GRAIN_PARENT_GEOGRAPHY_LEGS + for code in codes: + assert legs[code] == (code,) + assert legs["K02000001"] == tuple(codes) + assert legs["K03000001"] == (*english, "W92000004", "S92000003") + assert legs["E92000001"] == tuple(english) + assert UK_CROSS_GRAIN_RULE.parent_geography_legs is legs + assert "England" not in {leg for legs_ in legs.values() for leg in legs_} + + +def test_uk_cross_grain_leg_of_area_resolves_regions_nations_and_refuses() -> None: + # Tier codes are their own leg. + assert _uk_cross_grain_leg_of_area("E12000007") == "E12000007" + assert _uk_cross_grain_leg_of_area("W92000004") == "W92000004" + # Nations resolve by GSS prefix without a crosswalk lookup. + assert _uk_cross_grain_leg_of_area("W07000041") == "W92000004" + assert _uk_cross_grain_leg_of_area("S14000001") == "S92000003" + assert _uk_cross_grain_leg_of_area("N06000001") == "N92000002" + # English areas resolve through the crosswalk's ladder-derived membership. + assert _uk_cross_grain_leg_of_area("E14001073") == "E12000007" + assert _uk_cross_grain_leg_of_area("E09000001") == "E12000007" + assert _uk_cross_grain_leg_of_area("E06000001") == "E12000001" + with pytest.raises(ValueError, match="not in the local-area crosswalk"): + _uk_cross_grain_leg_of_area("E14000001") + with pytest.raises(ValueError, match="Unknown UK area code prefix"): + _uk_cross_grain_leg_of_area("X12345678") + with pytest.raises(ValueError, match="must not be blank"): + _uk_cross_grain_leg_of_area(" ") + + +def test_align_uk_national_registry_parity_fixture_renames_regional_rows() -> None: + fixture = { + "rows": [ + {"name": "ons/london_age_0_9", "value": 1.0, "period": 2025}, + {"name": "ons/yorkshire_and_the_humber_age_80_89", "value": 2.0}, + {"name": "ons/northern_ireland_age_10_19", "value": 3.0}, + {"name": "voa/council_tax/NORTH_EAST/A", "value": 4.0}, + {"name": "voa/council_tax/LONDON/total", "value": 5.0}, + {"name": "voa/council_tax/WALES/A", "value": 6.0}, + {"name": "ons/female_0_4", "value": 7.0}, + {"name": "ons/uk_population", "value": 8.0}, + "not-a-row", + ] + } + aligned = align_uk_national_registry_parity_fixture(fixture) + assert [ + row if isinstance(row, str) else row["name"] for row in aligned["rows"] + ] == [ + "ons.population.age_0_9_by_region@E12000007", + "ons.population.age_80_89_by_region@E12000003", + "ons.population.age_10_19_by_region@N92000002", + "voa.council_tax_stock.band_a@E12000001", + "voa.council_tax_stock.total@E12000007", + "voa/council_tax/WALES/A", + "ons/female_0_4", + "ons/uk_population", + "not-a-row", + ] + london = aligned["rows"][0] + assert london["measure"] == london["name"] + assert london["contract_target_id"] == "ons.population.age_0_9_by_region" + assert london["value"] == 1.0 and london["period"] == 2025 + # The input is never mutated. + assert fixture["rows"][0]["name"] == "ons/london_age_0_9" + + +def _region_tier_control_spec( + area: str, + value: float, + *, + ledger_level: str, + cross_grain_grain: str | None, +) -> TargetSpec: + metadata = { + "contract_target_id": "ons.population.age_0_9_by_region", + "ledger_geography_level": ledger_level, + "ledger_geography_id": area, + } + if cross_grain_grain is not None: + metadata["cross_grain_grain"] = cross_grain_grain + return TargetSpec( + name=f"ons.population.age_0_9_by_region@{area}", + entity="person", + value=value, + measure=f"ons.population.age_0_9_by_region@{area}", + period=2025, + source="synthetic region tier", + family="ons_population", + metadata=metadata, + ) + + +def _local_age_spec(area: str, value: float) -> TargetSpec: + return TargetSpec( + name=f"ons.age.0_10@{area}", + entity="person", + value=value, + measure="age/0_10", + period=2025, + source="synthetic local age", + family="ons_population", + metadata={ + "contract_target_id": "ons.age.0_10", + "geography_level": "constituency", + "geography_id": area, + }, + ) + + +def test_uk_local_target_surface_places_nation_rows_at_the_region_grain() -> None: + """Chronicle stamps Wales at ``country``; the stamp must not outrank London.""" + + registry = TargetRegistry( + [ + _region_tier_control_spec( + "E12000007", 100.0, ledger_level="region", cross_grain_grain="region" + ), + _region_tier_control_spec( + "W92000004", 40.0, ledger_level="country", cross_grain_grain="region" + ), + _local_age_spec("E14001073", 60.0), + _local_age_spec("W07000041", 30.0), + ], + country="uk", + ) + surface, receipt = uk_local_target_surface( + registry, + bound_national_target_ids=("ons.population.age_0_9_by_region",), + period=2025, + ) + age = surface.loc[surface["metric"] == "age/0_10"] + assert age["value"].tolist() == pytest.approx([100.0, 40.0]) + group = next( + group + for group in receipt["groups"] + if group["bridge_id"] == "national_age_0_9_vs_local_age_0_10" + ) + assert group["winning_grain"] == "region" + assert [leg["parent_geography_id"] for leg in group["legs"]] == [ + "E12000007", + "W92000004", + ] + + +def test_uk_local_target_surface_refuses_a_nation_row_left_at_country_grain() -> None: + registry = TargetRegistry( + [ + _region_tier_control_spec( + "E12000007", 100.0, ledger_level="region", cross_grain_grain="region" + ), + _region_tier_control_spec( + "W92000004", 40.0, ledger_level="country", cross_grain_grain=None + ), + _local_age_spec("E14001073", 60.0), + _local_age_spec("W07000041", 30.0), + ], + country="uk", + ) + with pytest.raises(ValueError, match="unparented lower-grain leg"): + uk_local_target_surface( + registry, + bound_national_target_ids=("ons.population.age_0_9_by_region",), + period=2025, + ) + + +def test_uk_local_target_surface_refuses_an_unknown_cross_grain_grain() -> None: + registry = TargetRegistry( + [ + _region_tier_control_spec( + "E12000007", 100.0, ledger_level="region", cross_grain_grain="itl2" + ), + _local_age_spec("E14001073", 60.0), + ], + country="uk", + ) + with pytest.raises(ValueError, match="cross_grain_grain 'itl2'"): + uk_local_target_surface( + registry, + bound_national_target_ids=("ons.population.age_0_9_by_region",), + period=2025, + ) diff --git a/packages/microcosm-build/tests/test_uk_local_area_crosswalk.py b/packages/microcosm-build/tests/test_uk_local_area_crosswalk.py index 1a2a9e800..f4b9b8429 100644 --- a/packages/microcosm-build/tests/test_uk_local_area_crosswalk.py +++ b/packages/microcosm-build/tests/test_uk_local_area_crosswalk.py @@ -9,6 +9,7 @@ import pytest from microcosm.build.country_spec import load_country_spec +from microcosm.build.uk_runtime.geography_ladder import UK_REGION_TIER from tools.generate_uk_local_area_crosswalk import build_local_area_crosswalk LADDER_ARTIFACT = Path("build/uk/uk_oa_ladder_2021.npz") @@ -102,3 +103,28 @@ def test_uk_local_area_crosswalk_pins_rosters_and_vintages() -> None: "S12", "W06", } + + +def test_uk_local_area_crosswalk_carries_region_tier_membership() -> None: + resource = _load() + tier = {code for _, code in UK_REGION_TIER} + nation_by_prefix = {"W": "W92000004", "S": "S92000003", "N": "N92000002"} + for payload in resource["levels"].values(): + by_area = payload["region_code_by_area"] + assert set(by_area) == set(payload["area_ids"]) + assert set(by_area.values()) <= tier + for area_id, region in by_area.items(): + if area_id.startswith("E"): + assert region.startswith("E12"), (area_id, region) + else: + assert region == nation_by_prefix[area_id[0]], (area_id, region) + constituency = resource["levels"]["constituency"]["region_code_by_area"] + local_authority = resource["levels"]["local_authority"]["region_code_by_area"] + # London: 75 constituencies and 33 authorities (32 boroughs + the City); + # the North East holds 12 authorities. Nation counts follow the rosters. + assert sum(region == "E12000007" for region in constituency.values()) == 75 + assert sum(region == "E12000007" for region in local_authority.values()) == 33 + assert sum(region == "E12000001" for region in local_authority.values()) == 12 + assert sum(region == "S92000003" for region in local_authority.values()) == 32 + assert sum(region == "W92000004" for region in local_authority.values()) == 22 + assert sum(region == "N92000002" for region in local_authority.values()) == 11 diff --git a/tools/build_uk_ledger_compile_parity_signed_differences.py b/tools/build_uk_ledger_compile_parity_signed_differences.py index 0a3f2acf6..0f798f0f8 100644 --- a/tools/build_uk_ledger_compile_parity_signed_differences.py +++ b/tools/build_uk_ledger_compile_parity_signed_differences.py @@ -12,6 +12,7 @@ from microcosm.build.uk_runtime.ledger_targets import ( LOCAL_REGISTRY_PARITY_FIXTURE_RESOURCE, align_uk_local_registry_parity_fixture, + align_uk_national_registry_parity_fixture, compile_uk_local_target_registry, compile_uk_target_registry, ) @@ -382,6 +383,18 @@ def _aligned_fixture(fixture: dict) -> dict: return aligned +_REGION_TIER_ONS_DRIFT_RATIONALE = ( + "Region-tier ONS population cell (microcosm#905): the Ledger row binds the " + "ONS mid-year estimate by single year of age at Chronicle's region or " + "country stamp, held to the comparison period as identity, while the " + "incumbent's regional row is an ONS subnational projection rounded to the " + "nearest thousand. The difference is projection-versus-estimate vintage " + "and rounding, never area or band definition: the nine English regions " + "and three nations sum exactly to the retired UK-wide row of the same " + "publication." +) + + def _add_signed_rationale_notes( report: dict[str, object], *, @@ -414,6 +427,8 @@ def _add_signed_rationale_notes( "baseline; it does not redatum the observations to the comparison " "year or create a new year ruling." ) + elif "_by_region@" in name and row.get("kind") == "calibration_drift": + row["reason"] = _REGION_TIER_ONS_DRIFT_RATIONALE elif fixture_resource != "registry_parity_fixture_2025.json": continue elif name in _CGT_OBSERVED_RATIONALES: @@ -475,7 +490,7 @@ def _fixture_for_receipt(spec: ParityReceiptSpec) -> dict: fixture = _aligned_fixture(_load_fixture(spec.fixture_resource)) if spec.surface == "local": return align_uk_local_registry_parity_fixture(fixture) - return fixture + return align_uk_national_registry_parity_fixture(fixture) def main() -> None: diff --git a/tools/generate_uk_local_area_crosswalk.py b/tools/generate_uk_local_area_crosswalk.py index c35bd022e..938e2fcc0 100644 --- a/tools/generate_uk_local_area_crosswalk.py +++ b/tools/generate_uk_local_area_crosswalk.py @@ -11,6 +11,11 @@ import numpy as np +from microcosm.build.uk_runtime.geography_ladder import ( + UK_LADDER_NATION_REGION_CODES, + UK_REGION_TIER, +) + DEFAULT_LADDER_ARTIFACT = Path("build/uk/uk_oa_ladder_2021.npz") DEFAULT_LADDER_SUMMARY = Path("build/uk/ladder_summary.json") DEFAULT_OUTPUT = Path( @@ -131,13 +136,19 @@ def _level_payload( ) -> dict[str, Any]: if code_column not in payload.files: raise ValueError(f"UK OA ladder artifact is missing {code_column!r}.") - area_ids = sorted({str(value) for value in np.asarray(payload[code_column])}) + if "region_code" not in payload.files: + raise ValueError("UK OA ladder artifact is missing 'region_code'.") + codes = np.asarray(payload[code_column]).astype(str) + area_ids = sorted(set(codes)) expected = EXPECTED_COUNTS[level] if len(area_ids) != expected: raise ValueError( f"UK OA ladder {level} roster has {len(area_ids)} area id(s), " f"expected {expected}." ) + region_code_by_area = _region_tier_by_area( + codes, np.asarray(payload["region_code"]).astype(str), level=level + ) layers = metadata.get("layers") or {} layer = layers.get(ladder_layer) or {} ladder_vintage = str(layer.get("vintage") or "") @@ -172,9 +183,42 @@ def _level_payload( "expected_vintage": EXPECTED_FACT_VINTAGE[level], "area_count": len(area_ids), "area_ids": area_ids, + "region_code_by_area": region_code_by_area, } +def _region_tier_by_area( + codes: np.ndarray, + regions: np.ndarray, + *, + level: str, +) -> dict[str, str]: + """Map every area id to the one region-tier code its output areas carry. + + The ladder stamps each OA with a region code (``E12`` for England, the + nation pseudo-codes elsewhere). The region tier nests constituencies and + authorities exactly, so an area whose OAs disagree is a ladder defect and + refuses here rather than becoming a leg that belongs to two controls. + """ + + tier_codes = {code for _, code in UK_REGION_TIER} + region_by_area: dict[str, str] = {} + for area_id, region in zip(codes.tolist(), regions.tolist(), strict=True): + tier = UK_LADDER_NATION_REGION_CODES.get(region, region) + if tier not in tier_codes: + raise ValueError( + f"UK OA ladder {level} area {area_id!r} carries region code " + f"{region!r}, which is outside the region tier." + ) + previous = region_by_area.setdefault(area_id, tier) + if previous != tier: + raise ValueError( + f"UK OA ladder {level} area {area_id!r} spans region-tier codes " + f"{previous!r} and {tier!r}; the region tier must nest." + ) + return dict(sorted(region_by_area.items())) + + def _sha256(path: Path) -> str: digest = hashlib.sha256() with path.open("rb") as file: From a564ef4aed63de368f69ebe9a371c236a6cee6a6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mar=C3=ADa=20Juaristi?= <127882282+juaristi22@users.noreply.github.com> Date: Fri, 11 Sep 2026 16:48:15 +0200 Subject: [PATCH 10/10] Record the region-tier calibration comparison on the spine-q pool (#905) Two tools/calibrate_uk_national_dataset.py runs on spine-q.h5 with the same overrides (epochs 1500, family_equal, lr 0.02): this branch's 595-reference register against PR #904's 424-reference register. All 108 ONS region cells and 78 of 81 VOA cells fit within 5% (median |error| < 0.1%); the three band-H cells with no band-H household on the spine sit at -100%, the class already signed for the local band-H family. Common-row fit is unchanged within run noise. Co-Authored-By: Claude Fable 5.1 --- .../calibration-comparison.json | 1309 +++++++++++++++++ 1 file changed, 1309 insertions(+) create mode 100644 docs/evidence/uk-region-tier-905/calibration-comparison.json diff --git a/docs/evidence/uk-region-tier-905/calibration-comparison.json b/docs/evidence/uk-region-tier-905/calibration-comparison.json new file mode 100644 index 000000000..78b2096f4 --- /dev/null +++ b/docs/evidence/uk-region-tier-905/calibration-comparison.json @@ -0,0 +1,1309 @@ +{ + "issue": "microcosm#905", + "pr": "microcosm#906", + "arm": { + "tool": "tools/calibrate_uk_national_dataset.py", + "input_h5": "spine-q.h5 (sha256 cf1f9dda198a06819241ed3358f68dd99c675eb7ca45fa766a3ed4b07eb0337f, the 2026-09-10 spine-q national pool)", + "chronicle_feed": "c6f9361 (facts 45bda3ae730d4ae3\u2026)", + "doctrine_overrides": { + "epochs": 1500, + "target_weight_rule": "family_equal", + "learning_rate": 0.02 + }, + "measure_exclusions": "packaged register (50 entries)", + "baseline": "PR #904 head a256c880 register (424 references, 374 after exclusions), run from the same code line with the same overrides", + "candidate": "this branch (595 references, 545 after exclusions)", + "run_date": "2026-09-11" + }, + "summary": { + "new": { + "final_loss": 0.012833430074165722, + "fraction_within_10pct": 0.9651376146788991, + "effective_sample_size": 10469.196661388754, + "n_nonzero": 52846, + "n_targets": 545, + "epochs": 1500, + "lr": 0.02, + "rule": "family_equal" + }, + "base": { + "final_loss": 0.011201580832300414, + "fraction_within_10pct": 0.9572192513368984, + "effective_sample_size": 10350.410607719752, + "n_nonzero": 52846, + "n_targets": 374, + "epochs": 1500, + "lr": 0.02, + "rule": "family_equal" + } + }, + "region_cells": { + "ons_population": { + "count": 108, + "bands": { + "within_5pct": 108 + }, + "median_abs_relative_error": 0.0008058624515383759, + "max_abs_relative_error": 0.0053138121847288 + }, + "council_tax_stock": { + "count": 81, + "bands": { + "within_5pct": 78, + "outside_25pct": 3 + }, + "median_abs_relative_error": 0.0008923704864915138, + "max_abs_relative_error": 1.0 + } + }, + "region_cells_outside_bound": [ + { + "name": "voa.council_tax_stock.band_h@E12000001@2025", + "target": 1570.0, + "final_estimate": 0.0, + "relative_error": -1.0 + }, + { + "name": "voa.council_tax_stock.band_h@E12000002@2025", + "target": 7350.0, + "final_estimate": 0.0, + "relative_error": -1.0 + }, + { + "name": "voa.council_tax_stock.band_h@E12000003@2025", + "target": 3770.0, + "final_estimate": 0.0, + "relative_error": -1.0 + } + ], + "common_rows": { + "count": 356, + "abs_relative_error_bands": { + "base": { + "\u22645%": 332, + "\u226425%": 16, + "\u226410%": 8 + }, + "new": { + "\u22645%": 329, + "\u226425%": 15, + "\u226410%": 11, + ">25%": 1 + } + }, + "largest_movers": [ + { + "name": "obr.fuel_duties_cars@2025", + "baseline_relative_error": -0.14874191635126816, + "candidate_relative_error": -0.2387073236943142 + }, + { + "name": "dwp.uc.households_children_2@2025", + "baseline_relative_error": -0.07423798754008737, + "candidate_relative_error": -0.11569793290357122 + }, + { + "name": "hmrc/self_employment_income_income_band_50_000_to_70_000@2025", + "baseline_relative_error": 0.10902101291923244, + "candidate_relative_error": 0.07925852300729519 + }, + { + "name": "hmrc/state_pension_income_band_50_000_to_70_000@2025", + "baseline_relative_error": 0.04892218279337719, + "candidate_relative_error": 0.07014864401180938 + }, + { + "name": "hmrc/employment_income_income_band_50_000_to_70_000@2025", + "baseline_relative_error": 0.005328092560829776, + "candidate_relative_error": 0.02569959647002753 + }, + { + "name": "dwp.uc.households_children_1@2025", + "baseline_relative_error": -0.1808694470241334, + "candidate_relative_error": -0.1624071771211329 + }, + { + "name": "hmrc/employment_income_count_income_band_50_000_to_70_000@2025", + "baseline_relative_error": 0.006782008244633626, + "candidate_relative_error": 0.02216570303239588 + }, + { + "name": "hmrc/self_employment_income_income_band_20_000_to_30_000@2025", + "baseline_relative_error": 0.23605705188216192, + "candidate_relative_error": 0.25002720010212287 + }, + { + "name": "hmrc/state_pension_count_income_band_50_000_to_70_000@2025", + "baseline_relative_error": -0.018344147739413747, + "candidate_relative_error": -0.0047736985374449556 + }, + { + "name": "obr.child_benefit@2025", + "baseline_relative_error": 0.17894631373311598, + "candidate_relative_error": 0.1916386692045426 + }, + { + "name": "hmrc/state_pension_income_band_30_000_to_40_000@2025", + "baseline_relative_error": 0.13063666449127925, + "candidate_relative_error": 0.14228621539638722 + }, + { + "name": "hmrc/private_pension_income_income_band_300_000_to_500_000@2025", + "baseline_relative_error": -0.1018267139192942, + "candidate_relative_error": -0.11249834732979813 + } + ] + }, + "terminal_gate_messages": { + "candidate": [ + "microcosm_uk_2024: voa.council_tax_stock.band_h@E12000001@2025 relative error -100.0% exceeds 25%.", + "microcosm_uk_2024: voa.council_tax_stock.band_h@E12000002@2025 relative error -100.0% exceeds 25%.", + "microcosm_uk_2024: voa.council_tax_stock.band_h@E12000003@2025 relative error -100.0% exceeds 25%.", + "microcosm_uk_2024: hmrc/self_employment_income_income_band_20_000_to_30_000@2025 relative error +25.0% exceeds 25%.", + "Stale reviewed target-fit exclusions are back inside the bound; remove them: ['obr.fuel_duties_cars@2025']." + ], + "baseline": [] + }, + "reading": [ + "Every ONS region cell and 78 of 81 VOA cells fit within 5%; the tier binds as published.", + "The three band-H cells at -100% (North East, North West, Yorkshire and the Humber) have no band-H household on the spine (spine-m carried 170 band-H households from 49 raw FRS households, none in those regions; microcosm#762 A14), the class already signed for the local band-H family; they need a reviewed measure exclusion or a spine vintage with broader band-H support (microcosm#796).", + "Common-row fit is unchanged within run noise (332->329 within 5%, 16->15 within 25%); hmrc/self_employment_income_income_band_20_000_to_30_000 sits at the 25% bound (+23.6% -> +25.0%) and obr.fuel_duties_cars moves -14.9% -> -23.9% while staying inside its reviewed target-fit exclusion, which the gate now reports as stale; both belong to the #890/PR-S lane.", + "The baseline run refused to stage for want of MICROCOSM_UK_TERMINAL_GATE_SIGNING_KEY (an unsigned full-scale run under the #904 round-2 runner); its solve and diagnostics completed." + ], + "region_cell_rows": [ + { + "name": "ons.population.age_0_9_by_region@E12000001@2025", + "target": 285165.0, + "final_estimate": 285503.6717033386, + "relative_error": 0.0011876341884124034 + }, + { + "name": "ons.population.age_0_9_by_region@E12000002@2025", + "target": 865892.0, + "final_estimate": 866269.0863260374, + "relative_error": 0.0004354888670150748 + }, + { + "name": "ons.population.age_0_9_by_region@E12000003@2025", + "target": 626938.0, + "final_estimate": 627769.9211111069, + "relative_error": 0.0013269591428608132 + }, + { + "name": "ons.population.age_0_9_by_region@E12000004@2025", + "target": 543004.0, + "final_estimate": 543452.6816167831, + "relative_error": 0.0008262952331532403 + }, + { + "name": "ons.population.age_0_9_by_region@E12000005@2025", + "target": 718837.0, + "final_estimate": 719058.727563858, + "relative_error": 0.0003084531873818852 + }, + { + "name": "ons.population.age_0_9_by_region@E12000006@2025", + "target": 744698.0, + "final_estimate": 745749.9893870722, + "relative_error": 0.001412638931583215 + }, + { + "name": "ons.population.age_0_9_by_region@E12000007@2025", + "target": 1054493.0, + "final_estimate": 1054461.0709514618, + "relative_error": -3.0279052149429163e-05 + }, + { + "name": "ons.population.age_0_9_by_region@E12000008@2025", + "target": 1059434.0, + "final_estimate": 1059683.762228012, + "relative_error": 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