fact_ks4_national_averages is computed once at dbt build time (covered by
the EES DAG's stg_ees_ks4+ selector). _national_averages_payload now reads
both national-averages marts instead of scanning the performance dataframe
per year on every /api/compare request (~250ms saved per call). Fallback
for the deploy-before-DAG window computes the latest year only.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0146VHeLAWjDVE2B5uU67jCB
The AI review gate caught that stg_ees_ks2's 7 new columns broke the
positional UNION ALL with stg_legacy_ks2 in int_ks2_with_lineage, and
that the lineage CTEs never emitted them (same class of bug fixed for
KS4 in 34a5de2). Legacy gets typed null placeholders at matching
positions; both lineage CTEs pass the columns through. 45/45 columns
verified name-identical in order across both union branches.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0146VHeLAWjDVE2B5uU67jCB
Evidence trail for the rc_* mapping in the prior commit: real value_counts()
over the 7 MI report-card columns, confirming the 5-value grade vocabulary
and that 'Achievement'/'Safeguarding standards' match by exact string only
(no legacy OEIF column accidentally consumed).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0146VHeLAWjDVE2B5uU67jCB
Wires the tap TODO in stg_ofsted_inspections.sql: maps the 7 confirmed
report-card MI columns (Safeguarding standards, Inclusion, Curriculum
and teaching, Achievement, Attendance and behaviour, Personal
development and wellbeing, Leadership and governance) into rc_*
fields, parsed via the new parse_report_card_grade macro against
real sampled grade values (Exceptional/Strong standard/Expected
standard/Needs attention/Urgent improvement). rc_safeguarding_met
becomes boolean from Met/Not met. rc_early_years/rc_sixth_form have
no MI column yet and are intentionally omitted from COLUMN_PRIORITY,
staying NULL.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0146VHeLAWjDVE2B5uU67jCB
DfE never published school-level KS2 2021/22 data publicly (confirmed via
EES release notes and by walking the Compare School Performance download
wizard, which has no ks2 checkbox for 2021-2022, same as the COVID-cancelled
2020-2021 year). No archive exists to verify column headers against or
upload to the filebrowser; Task 6 is blocked at the source-data level.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0146VHeLAWjDVE2B5uU67jCB
Widen the year filter in stg_ees_ks2_national.sql from >= 201617 to
>= 201516 so the England national-averages line no longer starts a
year late; the catalogue CSV has a real, comparable 201516 row (2015/16
was the first year of the current expected-standard tests, so it's the
correct floor).
GPS/science/scaled-score national columns confirmed present at source
with correct mapping; prod NULLs are stale raw data, backfilled by the
next extract run. No _KS2_NATIONAL_COL_MAP change accompanies this fix.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0146VHeLAWjDVE2B5uU67jCB
int_ks4_with_lineage.sql unions stg_ees_ks4 and stg_legacy_ks4 via
`select *`, which PostgreSQL aligns positionally. stg_legacy_ks4 listed
sen_support_pct before sen_ehcp_pct while stg_ees_ks4 lists sen_ehcp_pct
before sen_support_pct, swapping the two values for legacy-sourced rows
in marts.fact_ks4_performance. Reordered stg_legacy_ks4's final select
to match.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0146VHeLAWjDVE2B5uU67jCB
ReligiousCharacter 99 (~4k schools) and AdmissionsPolicy 9 (~5.6k) carry a
code with a blank name in the GIAS CSV; the generator skipped them so they
hit the Unknown(<code>) path — wrongly triggering the Faith-priority tag
and polluting filters. Blank-only codes now map to "" (byte-identical to
the old name pipeline); accepted_values lists extended to match the seed.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Addresses AI-review findings: the annual IDACI DAG also rebuilds a mart
(fact_deprivation) and needs the reload; curl gets connect/max timeouts
so an unreachable backend fails fast instead of hanging the task.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The daily/monthly/annual DAG docstring promised an Invalidate Cache step
that never existed — after a marts rebuild the backend kept serving its
startup-cached (possibly empty) DataFrame until a container restart.
Add a POST /api/admin/reload task at the end of each pipeline DAG,
mirroring the sitemap DAG's admin-call pattern.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
These schools are still operating and publish results; they drop out
automatically when GIAS flips them to Closed since marts fully rebuild
each run.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Matches the phase derivation's guard against casing/whitespace variants in
raw GIAS data; an unmatched variant previously fell through silently to the
statutory-age fallback.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Removes the 'What Parents Say' section and all supporting elements:
Frontend:
- Drop the OfstedParentView type, the parent_view field, the survey
section and the 'X% would recommend' callouts in the primary and
secondary detail views, the Parents nav item, and the parent-view CSS.
Backend:
- Remove the FactParentView model, its loading in data_loader, and
parent_view from the school-details API response.
- Bump SCHEMA_VERSION to 6 and add an idempotent drop step
(DROP TABLE IF EXISTS marts.fact_parent_view) to the CLI migration;
add scripts/sql/drop_fact_parent_view.sql to apply directly to the
dbt-owned marts DBs on staging and prod.
Pipeline:
- Delete the stg_parent_view + fact_parent_view dbt models and their
source/schema entries, the tap-uk-parent-view Meltano extractor, and
the monthly Parent View DAG; drop it from the Dockerfile and the
staging bootstrap docs.
The rest of dbt (which builds every mart the app reads) is untouched.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
int_ofsted_latest is only ref()'d inside a conditional block, so dbt
couldn't infer the edge and failed to compile dim_school. Add the
-- depends_on hint dbt recommends. No runtime behaviour change: the
adapter.get_relation guard still handles the pre-Ofsted-pipeline case.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Ungraded (Section 8) inspections don't assign a fresh grade — the export
only gives free text like "School remains Good". Parse that text into a
grade (remains Outstanding -> 1, remains Good -> 2, else null) and use it
as a last-resort fallback when no graded overall effectiveness exists.
Also retain schools that have only an ungraded inspection (no graded date)
by coalescing the inspection date, so ~8.5k previously-dropped schools now
carry a grade.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The standalone dbt Fusion binary (dbt-core 2.x) on PATH shadows the
pip-installed classic dbt-postgres ~=1.10 and rejects the Postgres
adapter (dbt1005), breaking every DAG's dbt_build task. Invoke dbt via
`python -m dbt.cli.main` in the DAGs and the Dockerfile dbt deps step so
the classic Postgres-capable engine is always used regardless of PATH.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
int_ks4_with_lineage references stg_legacy_ks4 but the model was never
selected for build, causing a missing relation error.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
LegacyKS2Stream now auto-detects ZIP vs bare CSV — if the download is a ZIP
it extracts england_ks2final.csv; if it's a plain CSV file it reads directly.
This keeps backwards compatibility while allowing both streams to share the
same DfE annual archive URLs.
legacy_ks2_urls updated to point at the same 4 ZIPs as legacy_ks4_urls so
only one set of archives needs to be maintained going forward.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Mirrors the existing legacy KS2 pattern to fill the gap before EES hosted
KS4 data. Four files changed:
- tap-uk-ees: LegacyKS4Stream downloads each year's DfE Compare School
Performance ZIP, extracts england_ks4final.csv, maps 416 legacy columns
to Singer fields, strips % suffixes. Registered in discover_streams().
TapUKEES.config_jsonschema gains legacy_ks4_urls setting.
- stg_legacy_ks4.sql: safe_numeric casts + NULL placeholders for columns
not present in legacy format (ebacc_avg_score, gcse_grade_91_pct,
prior_attainment_avg, sen_pct).
- int_ks4_with_lineage.sql: adds all_ks4 CTE unioning stg_ees_ks4 and
stg_legacy_ks4, matching the int_ks2_with_lineage pattern.
- _stg_sources.yml + meltano.yml: source declaration and setting definition
for legacy_ks4. URLs configured per-year once provided.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Two bugs prevented historical secondary school data from loading:
1. stg_ees_ks4.sql filtered breakdown_topic = 'Total' only, but EES
releases prior to 2023/24 use breakdown_topic = 'All pupils' (matching
the KS2 convention). All older years were silently dropped to zero rows.
Fix: accept both values with an IN clause.
2. get_all_releases() in tap-uk-ees fetched only the first page of the
EES releases API. Now follows all pages via the paging.totalPages field
so no historical release is missed when more than 20 exist.
After re-running the annual EES pipeline, secondary school comparison
charts should show data across all available years (2018/19 onwards).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The staging model aliased EES's total_number_places_offered column as
published_admission_number, but PAN is the school's published capacity
(not exposed by EES at school level) — what we actually have is the
count of places offered in a given admissions round. The misnomer
propagated to the mart, SQLAlchemy model, API response, TS types, and
UI copy ("places per year", "(PAN)").
Rename end-to-end and fix the UI labels:
- "29 places for 42 first-choice applications"
→ "29 places offered for 42 first-choice applications"
- "Reception/Year 7 places per year"
→ "Reception/Year 7 places offered"
- drop the misleading "(PAN)" suffix in the secondary view
Also add a comment in stg_ees_admissions clarifying this is the number
of places offered, not PAN. Requires dbt to rebuild fact_admissions
(marts are materialized as tables) before the backend can start.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
dim_school.sql was checking for int_ofsted_latest in target.schema (wrong schema)
due to the custom generate_schema_name macro using literal schema names. The
model lives in 'intermediate', so ofsted_grade/date/framework were always NULL
in dim_school, causing all list cards to show 'Not yet inspected'.
Fix 1: data_loader.py joins marts.fact_ofsted_inspection with DISTINCT ON to
get latest inspection per school — no pipeline re-run needed.
Fix 2: dim_school.sql uses schema='intermediate' so future dbt runs correctly
denormalise the Ofsted summary into dim_school.
meltano run does not support --select; the full tap-uk-ees run already
includes EESKs2NationalStream so no separate task is needed.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Replaces computed means from our school dataset with the published DfE
national headline figures for the KS2 chart reference line.
- tap-uk-ees: new EESKs2NationalStream fetches the stable EES data-catalogue
CSV (one row per year, England national total, AllSchools filter)
- dbt staging: stg_ees_ks2_national normalises columns, casts to float,
filters to years >= 201617
- dbt mart: fact_ks2_national_averages — one row per year, official figures
- backend/models: Ks2NationalAverage SQLAlchemy model
- backend/app: /api/national-averages queries the mart for KS2 by_year;
secondary by_year stays computed (no DfE KS4 national dataset yet)
- DAG: extract_ks2_national task added to school_data_annual_ees,
runs in parallel with the main EES extract
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The staging model was mapping EES column ``proportion_1stprefs_v_totaloffers``
straight onto ``first_preference_offer_pct``. That raw column is not a
percentage — it is a ratio of first-preference applications to total offers
(an oversubscription indicator, >1 means oversubscribed), so OLQH rendered
as "1%" when the true first-choice success rate is 27/42 = 64%.
The frontend display code is not at fault and is not patched here —
data-quality issues must be fixed at the source.
- stg_ees_admissions: compute ``first_preference_offer_pct`` as
``100 * number_1st_preference_offers / times_put_as_1st_preference`` —
of families who listed this school first, the % that received an offer
(0–100). Guard against divide-by-zero.
- stg_ees_admissions: expose the legitimate EES ratio as the new column
``oversubscription_ratio`` (1st-preference applications per place) for
future use, clearly named.
- fact_admissions, FactAdmissions model, data_loader: propagate the new
``oversubscription_ratio`` column.
- SchoolAdmissions type: document both columns inline.
- buildSchoolSummary: reword the oversubscription clause so it reads
sensibly across the whole 0–100 range (no more "just 64%").
- Hero chip subtitle: clearer phrasing "X% of first-choice applicants
offered a place".
Requires a dbt run of stg_ees_admissions and fact_admissions on deploy
so the new column materialises.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The new phase inference can legitimately leave ~1100 independent schools with
null phase (no GIAS phase, no statutory ages, name gives no hint). That's a
known data quality gap, not a pipeline failure — the UI already handles null
by showing no pill. Downgrade the test to warn so it stays visible in dbt
output without blocking the DAG.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The case-insensitive "Not Applicable" fix caught schools where GIAS publishes
statutory ages, but some independent schools leave those blank too — they fall
through every branch and end up with null phase and no pill in the UI.
Add a third tier that infers phase from the school name
(Primary/Infant/Junior/Prep vs Secondary/High/Grammar/Senior/Upper) and also
normalise "Not Applicable" handling with trim() + "unknown"/"" exclusion, so
the final else branch can safely return null instead of the catch-all string.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Expand the abbreviation in metric names (backend schemas), the home page
sort dropdown, README/QA docs, and pipeline comments. Short_name fields
and the compact row/map-card labels remain abbreviated for space.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
GIAS provides 'Not Applicable' (capital A) but the check used 'Not applicable',
so the case-sensitive != matched true and skipped the age-range inference.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Independent schools have phase='Not applicable' in GIAS. Now infer
phase from statutory age range: <=11 → Primary, >=11 → Secondary,
spans both → All-through. Falls back to original value if no age data.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Old DfE CSVs encode percentages as "57%" not "57". The safe_numeric
macro rejects non-numeric strings, so strip the suffix before emitting.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Tudor
tudor