`backend/migration.py` and `scripts/migrate_csv_to_db.py` import `School`,
`SchoolResult`, `init_db` and `set_db_schema_version` — names that no longer
exist. `scripts/geocode_schools.py` imports the same removed ORM model. None of
the three can be imported against the current backend, so they were not dormant
utilities anyone could fall back on; they were files that would fail on the
first line. `backend/version.py` existed only to hand `SCHEMA_VERSION` to that
importer, and the FastAPI lifespan performs no version-triggered import.
Three symbols go with them, each confirmed to have no caller: the unvectorised
`haversine_distance`, superseded by the inline NumPy calculation in search;
`fetcher`, an SWR helper for a dependency this project does not install; and
`kmToMiles`. `calculateDistance` stays — CutoffMapPanel uses it.
Two comments pointed at `migrate_csv_to_db.py --drop` to explain why Payload
owns its own schema. The reason survives the script: blog content must stay
clear of the school marts and Airflow's metadata. Reworded rather than deleted,
so the constraint keeps its justification.
docs/LEGACY_CODE.md records what was removed and where to find it in history. It
also records what was deliberately *not* removed, which is the more useful half:
unused UI components awaiting a design decision, manual data utilities whose
operators a repository search cannot see, and fallbacks that look obsolete but
are load-bearing — `data_loader.py`'s older-mart branches, the generated GIAS
dictionary copies, and the `legacy`-named dbt models that annual DAG selectors
explicitly include. A zero-import count is evidence, not a verdict.
The scripts that fetch DfE CSVs are marked historical and kept, pending
confirmation that nobody runs them by hand.
Checked: 190 backend tests, 429 frontend tests, `tsc --noEmit` clean.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_016y2J6bs8gbuSJbH18w7Tan
Review found _mask_for_disclosure could return with its invariant broken
and say nothing. add_companion only ever withheld a *published* cell, so a
group with one suppressed category and every other one not_applicable —
routine in special schools and AP, where few categories apply — left the
loop with the lone suppressed cell still solvable. Reproduced on a
nine-pupil cohort: one hidden cell, cohort served, residual intact.
A disclosure-control pass that fails silently is worse than none, because
everything downstream trusts it. The loop now runs until the invariant
holds and escalates when no companion exists: the pupil group is dropped
from the payload, and an empty block serialises as None so the section is
absent rather than an empty shell. disclosure_invariant_holds() is exported
so tests assert it directly instead of re-deriving it, and an exhaustive
test sweeps all 81 suppression patterns of a four-category group.
Also fixes a test that set up six measures and checked one: the loop was
`for measure in ["school_sixth_form"]`. It now checks every measure, and
against the real invariant — none hidden, or at least two, rather than
"at least two", which the five published measures would have failed.
No regression on real data: 262 mainstream secondaries, all-pupils bar
still drawable on 94%, zero invariant violations, one disadvantaged group
dropped by the new escalation.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01BvdDKvFFSZuMVDH5fEyTob
Code review found the disclosure the whole design was meant to prevent.
R1 was written as a rendering rule and implemented as one: canRenderBar
stopped the bar being drawn, but GET /api/schools/{urn} still carried the
cohort and every published category. cohort - sum(published) returned
Whitley Bay's withheld further-education figure exactly — 18 pupils — to
any caller, and the RSC payload put it in the browser too.
app.py already stated the principle for admission_distance: this endpoint
is public and unauthenticated, so a field left in the payload is a
published field. The same reasoning applies here and did not get applied.
_mask_for_disclosure now closes both identities before serialisation —
categories sum to the cohort, and disadvantaged + other = all — by adding
secondary suppression until every row and column hides none or at least
two. My first attempt picked the smallest published cell as the companion
and a new test caught it choosing a zero, which protects nothing: the
residual still resolved to 18. The companion must carry pupils.
DfE's own aggregates are no longer served. Nothing rendered them, and one
spanning a single suppressed component names it.
Cost, measured over 262 mainstream secondaries: the all-pupils bar
survives on 94% rather than 100%. Zero lone-suppressed groups remain.
The e2e helper now tells a missing feature apart from missing data: it
fails if the API serves no destinations key at all, and skips if the key
is served but the annual DAG has not populated the marts. Failing on the
second would redden the staging gate for unrelated commits.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01BvdDKvFFSZuMVDH5fEyTob
The serialiser carries status through and computes no totals of its own.
The only aggregates in the payload are ones DfE published itself; whether
showing one is safe depends on how many of its components are suppressed,
which the frontend decides.
The batch guard grows from six tables to eight.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01BvdDKvFFSZuMVDH5fEyTob
Code review, both findings valid.
The design doc claimed Cloudflare "replaces the header, so a browser
cannot forge it", and that only the X-Forwarded-For fallback was
forgeable. That is true only for traffic that actually passed through
Cloudflare, and nothing in this process can verify that it did. Reaching
the origin directly, both headers are equally attacker-controlled — and
rotating CF-Connecting-IP mints a fresh rate-limit bucket per request,
defeating per-client limits on every endpoint including the
DataFrame-heavy /api/schools. Against abuse that is worse than the
shared bucket it replaced, which at least capped everyone together.
So the ceiling comes back. I dropped it earlier arguing it belonged at
Cloudflare; that argument assumed the keying was sound, and it is not.
GlobalRateLimitMiddleware counts all /api/ traffic in a fixed window
against a total, independent of client identity, outermost so it refuses
before any work happens. Written by hand because slowapi cannot express
a global cap: default_limits and application_limits are both keyed by
key_func, and the latter needs middleware this app does not install.
It does not make the header trustworthy — it makes trusting it
survivable. The real fix is Authenticated Origin Pulls or an origin
firewall, now documented in DEPLOY.md as the open gap it is.
127.0.0.1 is exempt: the healthcheck curls localhost from inside the
container, and starving it would restart the container and turn a load
spike into an outage loop. Keyed on the peer address, never the Host
header, which the caller sets.
Second finding: suggest_schools_typesense promised "never raises" while
the parsing loop sat outside the try, so int(None) on a malformed
document would have made a keystroke a 500. The loop now skips bad rows
rather than dropping the whole list — and a hit with no document no
longer becomes a suggestion pointing at /school/0.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_015mWQnpye9F299NVRCCSRvj
search_schools_typesense returns URNs, which forces the caller to
hydrate from the 25,000-row in-memory frame. Every field a suggestion
needs is already in the Typesense document, so this returns documents
and the caller needs no pandas at all — the difference between a query
that can run per keystroke and one that cannot.
Never raises. Typesense unreachable or erroring gives an empty list,
because a dropdown that quietly stops appearing is the right failure for
a keystroke path.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_015mWQnpye9F299NVRCCSRvj
Earlier years are to become a paid feature, so they stop being published.
The load-bearing part is that this is a change to the API, not only to the
page. /api/schools/{urn} is public and unauthenticated: leaving
admission_distance_history in the payload while declining to render it would
have handed the whole record to anyone who opened the network tab. It is
withheld at the source, and the page follows.
Nothing changes upstream. The tap, the plausibility band and
fact_admission_distance are untouched and still load every published year, so
restoring history for entitled callers is a change to one function in
data_loader rather than a re-collection.
What the reader now gets is the latest figure on the Admissions tile, and a
Distance section that answers the question the number alone cannot: whether
their own address falls inside it. Retitled to "How far away are you?", which
is what it now does — the previous title described a record that is no longer
there.
Removed with the history: the trend chart, the year-by-year table, the
per-year verdict strip, the trend summary and the coverage note, along with
their CSS. The section goes from 743px to 417px.
One consequence worth naming. A run of years used to soften a single close
call — a home just outside one year's cut-off was usually inside another. With
one year published, the "too close to call" band is the entire safety margin
between a parent and a place they do not have, so the verdict now names its
year, and the three outcomes are tinted apart rather than distinguished by
wording alone.
The existing stylesheet test earned its keep here: the three verdict classes
were referenced before they were written, and it caught them. Unstyled, a
"beyond the cut-off" result would have been indistinguishable from an "inside"
one — the exact failure the longhand class map was written to prevent.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01WDvkyqqHABm4bmth2kjAxE
Completes the last-distance-offered feature against the mockup: the
year-by-year record, the same numbers drawn over real streets, and the
reader's own address measured against them.
Serving the history
The first cut deliberately served only the latest year, because a plain
series would draw a trend line straight through gaps that are absences of
publication, not of a cut-off. That reasoning is answered rather than
abandoned: cutoffYearRows classifies every year in the span, and the chart
breaks the line rather than interpolating across it.
A missing year is not one fact but three. It may be unpublished; it may be
a year the school was not oversubscribed; or there may be no record at all.
Collapsing them into "no data" throws away the reassuring case and hides
the important caveat, so each is stated in words in the table.
The claim is held to what the data supports. fact_admissions.oversubscribed
compares FIRST PREFERENCES against places, which does not establish that
every applicant was offered one — so the copy says "places available on
first preferences" and a test asserts the stronger claim never appears.
The trend summary is not a verdict
It names both endpoints and their years and lets the reader conclude. It is
withheld below four published points, and a swing under a tenth of the
earlier figure is reported as "broadly the same" rather than dressed up as
a direction.
The postcode check
This is the only place on the site that answers a question about a family
rather than a school, so most of the care went into what it refuses to say.
postcodes.io returns a centroid covering roughly fifteen addresses, which
against a 500 m cut-off is a fifth of the whole distance — so a margin
inside 100 m returns "too close to call" rather than a place a family does
not have. Unpublished years count as unknown, never as a pass. The limits
are stated before the check is used, not revealed with the answer.
The postcode is geocoded in the browser and never stored.
Both templates
Banded and selective secondaries are exactly where this matters most, so
the detail is shared. The primary page gives it a third tab; the secondary
page is one flat panel by design and renders it inline.
Absence is explained rather than reported. A selective school's missing
figure is explained by how it admits; a consistently undersubscribed school
reads as good news.
Also makes the batch loader's test double honour ORDER BY. It was a no-op,
so "latest row per URN" was really "first row in the fixture" and the test
would have passed with the sort reversed or removed.
Verified: 214 frontend tests, 54 backend, 45/53 e2e green against staging
(the 8 cut-off journeys skip until the DAG runs). Rendered offline against
the real compiled CSS in both themes and at 390px; every new surface clears
WCAG AA, measured on composited pixels.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01WDvkyqqHABm4bmth2kjAxE
Adds the cut-off distance a parent actually asks about — "how close do we
need to live?" — end to end: a Singer tap, dbt staging and mart models, an
Airflow DAG, and a tile on both detail templates. 3,597 schools across 57
local authorities carry a figure; the rest are unchanged.
There is no national source for this. Each LA publishes its own cut-offs in
its own format, and the collected CSV is transcribed from PDFs, spreadsheets
and web pages — so most of the work here is deciding what is safe to show.
Data
* tap-uk-school-distance loads the CSV verbatim into raw. Keyed on
(urn, year, school_name), because school_name carries the admission
route: (urn, year) alone collides on 118 keys and a reload would have
silently dropped every band but one.
* stg_school_distance applies a 25 m – 25 km plausibility band. The source
contains 0.0-mile rows (published where a school filled on a higher
criterion), 1-metre cut-offs, and one reading 533 miles — ~4% of rows,
all of which would put a visibly wrong number on a live page.
* fact_admission_distance collapses routes to one row per school per year
using the furthest, and keeps route_count so the page can say the figure
is the widest of several bands rather than the one for a given child.
Serving
* Kept out of fact_admissions: that mart is EES-derived and near-complete
for England, this one covers 57 LAs, and the two refresh independently.
* Latest year only. Coverage is ragged — a school may have 2021 and 2026
and nothing between — so a history array would invite a trend line drawn
through gaps that are absences of publication, not of a cut-off.
* The Admissions section now renders on either source. 3% of the schools
that render have a cut-off and no EES admissions row, and gating on
admissions alone would have hidden the figure on those pages.
Interface
* The year travels with the figure everywhere it appears; a cut-off
detached from its admissions round is not a fact about anything.
* "Not a fixed catchment — it moves every year" sits under every instance,
because that is the inference a parent will otherwise draw.
* Replaces a hardcoded "Historical distance cut-off data is not available
for this school" that appeared on every secondary page, including the
ones whose council does publish it. The absence is now stated only when
it is real, and names the authority that would hold it.
The tint costs the muted tokens their AA margin: measured on the composited
backdrop (not the computed one, which reports the untinted card), --text-muted
falls to 4.09:1 in dark theme. The tile uses --text-secondary instead — 6.50:1
dark, 6.60:1 light.
The DAG is manual, like the other annual ones: councils publish on allocation
day, each on its own timetable, so there is no date worth scheduling against.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01WDvkyqqHABm4bmth2kjAxE
The graceful-degradation fallback keys off the column named in a Postgres
UndefinedColumn error, but the matcher only stripped an `s.` alias. The two
new dim_location columns (county, parliamentary_constituency) are selected
via the `l.` alias and Postgres reports them unquoted as
"column l.county does not exist" — which the old regex failed to match at
all, returning None.
If dim_school is rebuilt (telephone/nursery present) but dim_location is not
yet (county/parliamentary_constituency missing) — plausible since they are
independently-rebuilt dbt models — the fallback branch never matched and
load_school_data_as_dataframe() returned an empty DataFrame, showing zero
schools sitewide instead of degrading those columns to NULL.
Generalise the alias prefix to `\w+\.` and cover the l.-qualified case in
tests.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Add seven school-identity fields to the detail header (both primary and
secondary views): age range, religious character, nursery and sixth-form
indicators as chips; telephone (tel: link), county and parliamentary
constituency as header details. religious_denomination, age_range and
has_sixth_form were already served; telephone, nursery_provision, county
and parliamentary_constituency are newly wired through the marts query
(with a NULL fallback for un-rebuilt marts, mirroring has_sixth_form) and
the school_info API response.
Remove three UI sections the backend never populated (always null): Year 1
Phonics, the SEN "types of additional needs" breakdown, and the average
class-size card — along with their now-dead props, route plumbing, and the
SenDetail/Phonics types + class_size_avg field.
Extend the e2e detail journey to assert the Phonics section is gone and the
new header fields render when the record carries them.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The search-result cards and map pins keyed report-card detection off
ofsted_framework === 'ReportCard', but the API sets ofsted_framework to the
raw event grouping ('Schools - S5'); worse, ofsted_grade (the carried-forward
legacy grade) was checked first and won. So report-card schools were badged
by their old grade — Barclay's pin/card read 'Outstanding · 2021' instead of
'Report Card · 2026'. Same root cause as the detail-page fix, different
surface.
Expose ofsted_rc_date on the list serialization (the report-card inspection
date, non-null only for report cards) and make both badge builders treat a
present rc-date as winning over any grade, using its year. Removes the dead
framework === 'ReportCard' branches.
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
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>
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 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>
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>
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>
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>
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>
- Backend: replace INNER JOIN ks2 with UNION ALL (ks2 + ks4) so primary
and secondary schools both appear in the main DataFrame
- Backend: add /api/national-averages endpoint computing means from live
data, replacing the hardcoded NATIONAL_AVG constant on the frontend
- Backend: add phase filter param to /api/schools; return phases from
/api/filters; fix hardcoded "phase": "Primary" in school detail endpoint
- Backend: add KS4 metric definitions (Attainment 8, Progress 8, EBacc,
English & Maths pass rates) to METRIC_DEFINITIONS and RANKING_COLUMNS
- Frontend: SchoolDetailView is now phase-aware — secondary schools show
a GCSE Results section (Att8, P8, E&M, EBacc) instead of SATs; phonics
tab hidden for secondary; admissions says Year 7 instead of Year 3;
history table shows KS4 columns; chart datasets switch for secondary
- Frontend: new MetricTooltip component (CSS-only ⓘ icon) backed by
METRIC_EXPLANATIONS — added to RWM, GPS, SEN, EAL, IDACI, progress
scores and all KS4 metrics throughout SchoolDetailView and SchoolCard
- Frontend: METRIC_EXPLANATIONS extended with KS4 terms (Attainment 8,
Progress 8, EBacc) and previously missing terms (SEN, EHCP, EAL, IDACI)
- Frontend: SchoolCard expands "RWM" to "Reading, Writing & Maths" and
shows Attainment 8 / English & Maths Grade 4+ for secondary schools
- Frontend: FilterBar adds Phase dropdown (Primary / Secondary / All-through)
- Frontend: HomeView hero copy updated; compact list shows phase-aware metric
- Global metadata updated to remove "primary only" framing
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
sync_typesense.py:
- Fix query string replacement: was matching 'ST_X(l.geom) as lng' but
QUERY_BASE uses 'l.longitude as lng' — KS2/KS4 lateral joins were
silently dropped on every sync run
backend:
- Add typesense_url/typesense_api_key settings to config.py
- Add search_schools_typesense() to data_loader.py — queries Typesense
'schools' alias, returns URNs in relevance order with typo tolerance;
falls back to empty list if Typesense is unavailable
- /api/schools: replace pandas str.contains with Typesense search;
results are filtered from the DataFrame and returned in relevance order;
graceful fallback to substring match if Typesense is down
requirements.txt: add typesense==0.21.0, numpy==1.26.4
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Pipeline:
- EES tap: split KS4 into performance + info streams, fix admissions filename
(SchoolLevel keyword match), fix census filename (yearly suffix), remove
phonics (no school-level data on EES), change endswith → in for matching
- stg_ees_ks4: rewrite to filter long-format data and extract Attainment 8,
Progress 8, EBacc, English/Maths metrics; join KS4 info for context
- stg_ees_admissions: map real CSV columns (total_number_places_offered, etc.)
- stg_ees_census: update source reference, stub with TODO for data columns
- Remove stg_ees_phonics, fact_phonics (no school-level EES data)
- Add ees_ks4_performance + ees_ks4_info sources, remove ees_ks4 + ees_phonics
- Update int_ks4_with_lineage + fact_ks4_performance with new KS4 columns
- Annual EES DAG: remove stg_ees_phonics+ from selector
Backend:
- models.py: replace all models to point at marts.* tables with schema='marts'
(DimSchool, DimLocation, KS2Performance, FactOfstedInspection, etc.)
- data_loader.py: rewrite load_school_data_as_dataframe() using raw SQL joining
dim_school + dim_location + fact_ks2_performance; update get_supplementary_data()
- database.py: remove migration machinery, keep only connection setup
- app.py: remove check_and_migrate_if_needed, remove /api/admin/reimport-ks2
endpoints (pipeline handles all imports)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Ofsted replaced single overall grades with Report Cards from Nov 2025.
Both systems are retained during the transition period.
- DB: new framework + 9 RC columns on ofsted_inspections (schema v4)
- Integrator: auto-detect OEIF vs Report Card from CSV column headers;
parse 5-level RC grades and safeguarding met/not-met
- API: expose all new fields in the ofsted response dict
- Frontend: branch on framework='ReportCard' to show safeguarding badge
+ 8-category grid; fall back to legacy OEIF layout otherwise;
always show inspection date in both layouts
- CSS: rcGrade1–5 and safeguardingMet/NotMet classes
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Adds a full data integration pipeline for enriching school profiles with
supplementary data from Ofsted, GIAS, EES, IDACI, and FBIT.
Backend:
- Bump SCHEMA_VERSION to 3; add 8 new DB tables (ofsted_inspections,
ofsted_parent_view, school_census, admissions, sen_detail, phonics,
school_deprivation, school_finance) plus GIAS columns on schools
- Expose all supplementary data via GET /api/schools/{urn}
- Enrich school list responses with ofsted_grade + ofsted_date
Integrator (new service):
- FastAPI HTTP microservice; Kestra calls POST /run/{source}
- 9 source modules: ofsted, gias, parent_view, census, admissions,
sen_detail, phonics, idaci, finance
- 9 Kestra flow YAMLs with scheduled triggers and 3× retry
Frontend:
- SchoolRow: colour-coded Ofsted badge (Outstanding/Good/RI/Inadequate)
- SchoolDetailView: 7 new sections — Ofsted sub-judgements, Parent View
survey bars, Admissions, Pupils & Inclusion / SEN, Phonics, Deprivation
Context, Finances
- types.ts: 8 new interfaces + extended School/SchoolDetailsResponse
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Add runtime normalization of cryptic school type codes to user-friendly names
(e.g., AC/ACC/ACCS -> "Academy", CY/CYS -> "Community")
- Update SCHOOL_TYPE_MAP in schemas.py with consolidated mappings
- Add normalize_school_type() and get_school_type_codes_for_filter() helpers
- Persist selected schools in localStorage across page refreshes
- Move "Add to Compare" button from modal footer to header
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
tudor