New ees_ks4_national stream ingests the EES 'National characteristics
summary data' series (England, state-funded, all pupils). The old mart's
unweighted school means were 7-15 points off every headline measure and
produced an impossible national Progress 8 (-0.27). The API's computed
fallback is gone too: the footnote calls these figures official, so an
unbuilt mart now yields an empty series, never a stand-in.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0146VHeLAWjDVE2B5uU67jCB
The FSM chip anchored against disadvantaged_pct (a different measure,
FSM6+CLA) whenever fsm_pct was null — which it always was, since the
performance df has no fsm_pct. New fact_census_benchmarks mart supplies
pupil-weighted FSM/EAL means per phase; the KS2-column medians that
produced a bogus 50% 'secondary disadvantaged' anchor are gone.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0146VHeLAWjDVE2B5uU67jCB
get_supplementary_data ran ~5 sequential DB round-trips per URN, so
/api/compare scaled at ~37ms/school (measured on staging: 1 school 155ms,
3 schools 220ms, 6 schools 340ms). get_supplementary_data_batch fetches
each table once with WHERE urn IN (...) and groups in Python, collapsing
5*N round-trips to a constant 5. get_supplementary_data is now a thin
wrapper so the detail endpoint is unchanged; the compare endpoint makes
one batched call. Each table degrades independently on failure.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0146VHeLAWjDVE2B5uU67jCB
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
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>
str(ProgrammingError) embeds the full SQL, which contains every column
name — the substring check matched any error and could take the wrong
retry branch. Parse the missing column from exc.orig instead.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Closes the deploy window flagged by CI review — the backend now works
against both the old (name) and new (code) mart schemas.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
- data_loader.load_school_data_as_dataframe now catches a ProgrammingError
whose message mentions has_sixth_form (psycopg2 UndefinedColumn) and
retries with a NULL-AS-has_sixth_form query variant, so the API keeps
serving data (and the app.py column-fallback branch stays reachable)
even before the nightly pipeline has rebuilt marts.dim_school.
- utils.convert_to_native now handles numpy.bool_ so GET /api/schools/{urn}
doesn't 500 once has_sixth_form is a populated bool-dtype column.
- Update the now-stale comment on the app.py age-range fallback branch.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Schools without KS2/KS4 results (special post-16 institutions, sixth-form
centres, PRUs, new schools) come back from the marts LEFT JOIN with NaN in
every numeric column. school_info passed those raw pandas values straight
into JSONResponse, which renders with allow_nan=False, so the detail
endpoint 500d and the frontend turned that into a 404 on every such SEO
landing page.
Run school_info values through convert_to_native (the same treatment
yearly_data already gets), add backend unit tests plus a pytest step in PR
checks, and an e2e journey that finds a results-less school via the search
API and asserts its page renders.
Co-Authored-By: Claude Opus 4.8 <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>
The rankings endpoint validated year with le=2100, but the database
stores academic-year codes like 201819, so any explicit year selection
returned a 422 and the rankings page rendered its empty state. Widen
the bound to cover the codes and extend the e2e journey to pick a
specific year and assert the table stays populated.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The detail page reads overall_effectiveness from the API and showed
"Not rated" for ungraded-only schools, even though the list badge uses
the coalesced grade. Coalesce the ungraded fallback into the API's
overall_effectiveness so the detail page shows the same grade.
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 school detail page only showed the latest admissions year. We store
every year, which is more decision-relevant for parents (the trend and its
consistency matter more than a single noisy year).
Backend now returns the full admissions_history (oldest first) alongside the
existing latest-year object. The primary SchoolDetailView gains a header
toggle ("This year | N-year trend") that swaps the Q&A for an SVG sparkline
of the first-choice offer rate. The toggle only appears when >=2 years carry
an offer rate; otherwise it falls back to the single-year card. Both views
share one CSS-grid cell so switching causes no layout shift.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Frontend
- Dynamic-import Chart.js components on detail/compare views so Chart.js
no longer ships in initial JS.
- Drop force-dynamic on home, compare, rankings so internal data fetches
reuse Next.js's per-call revalidate cache.
- Switch /school/[slug] to ISR with a 7-day revalidate window (school
data updates annually).
- Preconnect to analytics + postcodes.io; remove redundant defer on the
Umami Script tag (afterInteractive already covers it).
- Bump images.minimumCacheTTL to 1 year.
- Extract HowItWorks and Editorial sections as server components passed
to HomeView via slot props so their JSX stays out of the client bundle.
Backend
- Add GZipMiddleware (min 512 bytes).
- Add CacheAndETagMiddleware: per-path Cache-Control with long s-maxage
+ stale-while-revalidate, ETag generation, and 304 on If-None-Match.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The /api/rankings endpoint returned each row keyed by the metric's
column name (e.g. rwm_high_pct) but never under a generic `value`
field. The frontend RankingItem type and RankingsView both read
ranking.value, so every row rendered "—" for every metric — the
default rwm_expected_pct included.
Add `df["value"] = df[metric]` before JSON serialisation so the
frontend gets the value it has always expected. The raw metric
column is still in the row for any caller that wants it explicitly.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Upgrades the existing "Pupils" stat to include a compact split bar and
percentage hint for mixed schools (single-sex schools already carry a
"Boys's/Girls's school" badge, so the split would be redundant).
Wires fact_pupil_characteristics into the API: new SQLAlchemy model and
a real census block in /api/schools/{urn} replacing the prior null stub.
On the primary detail page the inline "Pupils: 241" text is replaced by
a richer block (display number + bar + "52% girls · 48% boys"). On the
secondary detail page the existing "Total pupils" hero stat card grows
the bar and hint beneath the number. Both fall back to the previous
text-only rendering when census gender data is missing.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
P1 (backend/data_loader.py): Add load_latest_school_data() which pre-computes
the one-row-per-school latest-year snapshot (groupby, prev-year trend merge)
once at startup instead of on every /api/schools request. get_schools route
now starts from the cached snapshot rather than rebuilding it.
S3 (backend/app.py): Wrap synchronous geocode_single_postcode() call in
asyncio.to_thread() so postcode lookups no longer block the uvicorn event
loop. Admin reload endpoint also uses to_thread for both cache primes.
P2 (nextjs-app/components/HomeView.tsx): Add mapParamsRef guard so switching
back to map view does not re-fetch 500 schools when search params haven't
changed. Reset ref on new searches so fresh data is always fetched.
P3 (nextjs-app/lib/chartSetup.ts): Extract Chart.js registration into a
shared side-effect module. ComparisonChart and PerformanceChart now import
it instead of each calling ChartJS.register() independently.
P4 (backend/database.py): Remove unnecessary db.commit() from the read-only
get_db_session() context manager — saves a DB round-trip on every request.
P5 (backend/database.py): Add pool_recycle=1800 to SQLAlchemy engine to
prevent stale TCP connections from accumulating in long-running processes.
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>
Redesign the School Details page for better parent comprehension:
- New SatsChart component: horizontal cascade bars with ruler scale and
national average marker (teal/coral palette matching site theme)
- Admissions section: visual progress bar showing 1st-preference demand
vs available places, colour-coded by oversubscription status
- Historical data: collapse raw year-by-year table behind a disclosure
element while keeping the performance line chart always visible
- EAL metric: add national average comparison via DeltaChip (backend now
includes eal_pct in national averages endpoint)
- New formatWithSuppression utility for null/suppressed data handling
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.
The school_info object was missing total_pupils entirely, so the frontend
always fell back to the KS4 exam cohort from yearly_data. Now selects
s.total_pupils (GIAS NumberOfPupils — full school roll) as gias_total_pupils
in the main query and exposes it as total_pupils on school_info.
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>
Previously the dashed reference line was a flat horizontal at the latest
year's national average across all historical data, implying the national
figure was constant. Now the backend returns per-year averages in `by_year`
and the chart maps each data year to its own national average, so the
reference line correctly reflects how the national picture changed over time
(including COVID recovery dip/recovery).
- backend: /api/national-averages now includes `by_year` list alongside
existing `year`/`primary`/`secondary` latest-year snapshot
- types: NationalAverages extended with `by_year: NationalAveragesYear[]`
- PerformanceChart: accepts `nationalByYear` prop; builds per-year series
aligned to school data years, falling back to scalar prop if absent
- SchoolDetailView + SecondarySchoolDetailView: pass `nationalAvg.by_year`
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>
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>
Root cause: the UNION ALL query in data_loader.py produced two rows per
all-through school per year (one KS2, one KS4), with drop_duplicates()
silently discarding the KS4 row. Fixes:
- New dbt mart `fact_performance`: FULL OUTER JOIN of fact_ks2_performance
and fact_ks4_performance on (urn, year). One row per school per year.
All-through schools have both KS2 and KS4 columns populated.
- data_loader.py: replace 175-line UNION ALL with a simple JOIN to
fact_performance. No more duplicate rows or drop_duplicates needed.
- sync_typesense.py: single LATERAL JOIN to fact_performance instead of
two separate KS2/KS4 joins.
- app.py: remove drop_duplicates (no longer needed); add PHASE_GROUPS
constant so all-through/middle schools appear in primary and secondary
filter results (were previously invisible to both); scope result_filters
gender/admissions_policies to secondary schools only.
- HomeView.tsx: isSecondaryView is now majority-based (not "any secondary")
and isMixedView shows both sort option sets for mixed result sets.
- school/[slug]/page.tsx: all-through schools route to SchoolDetailView
(renders both SATs + GCSE sections) instead of SecondarySchoolDetailView
(KS4-only). Dedicated SEO metadata for all-through schools.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Remove 10-mile radius option; cap backend radius max at 5 miles
- Raise backend page_size max to 500 so map can fetch all schools in one call
- HomeView: when map view is active, fetch all schools within radius
(page_size=500) instead of showing only the paginated first page;
falls back to initial SSR schools while loading
- SchoolMap/LeafletMapInner: accept referencePoint prop and render a
distinctive coral circle pin at the search postcode location
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Load-more requests read URL params (postcode, radius, etc.) but page_size
is never in the URL — it's hardcoded in page.tsx. Without it the backend
received page_size=None, hit a TypeError on (page-1)*None, returned 500,
and the silent catch left the user stuck on page 1.
In a dense area (e.g. Wimbledon SW19) 50 schools fit within ~1.8 miles,
so page 1 never shows anything beyond that regardless of selected radius.
Fix:
- Backend: give page_size a safe default of 25 instead of None
- Frontend: explicitly pass initialSchools.page_size in load-more params
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Backend builds sitemap.xml from school data at startup (in-memory)
- POST /api/admin/regenerate-sitemap refreshes it after data updates
- New Airflow DAG (sitemap_generate) runs Sundays 05:00 and calls the endpoint
- Next.js proxies /sitemap.xml to the backend; removes the slow dynamic sitemap.ts
- docker-compose passes BACKEND_URL + ADMIN_API_KEY to Airflow env
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
1. Simpler home page: only search box on landing, no filter dropdowns
2. Advanced filters: hidden behind toggle on results page, auto-open if active
3. Per-school phase rendering: each row renders based on its own data
4. Taller 4-line rows with context line (type, age range, denomination, gender)
5. Result-scoped filters: dropdown values reflect current search results
6. Fix blank filter values: exclude empty strings and "Not applicable"
7. Rankings: Primary/Secondary phase tabs with phase-specific metrics
8. Compare: Primary/Secondary tabs with school counts and phase metrics
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
tudor