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>
The KS2 SATs chart drew a single national-average line spanning the
full height of each subject's chart area, positioned at the national
*expected* value. But the area stacks two bars — Expected and Exceeding
— and the higher-standard/greater-depth national is a very different,
much lower figure (e.g. reading higher standard ~29% vs expected ~75%).
So the line crossed the Exceeding bar at the wrong place, making every
school's exceeding result look far below national when it wasn't.
The per-subject higher-standard nationals were already computed in the
fact_ks2_national_averages mart; they just weren't serialized. Fix:
- backend: add reading_high_pct, writing_gd_pct (writing = greater
depth) and maths_high_pct to the national-averages payload.
- SchoolDetailView: pass a nationalExceedingPct per subject, mapping
writing to the greater-depth figure.
- SatsChart: replace the single full-height line with a national marker
on each bar's own track (coral tick + "nat X%" in the bar header), so
Expected and Exceeding each sit against the correct benchmark.
KS2 only; the secondary Attainment 8 chart already uses one line for
one measure and is untouched.
Verified: tsc --noEmit, next build, and backend pytest (national
averages marts, incl. a new test guarding the per-subject nationals).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0146VHeLAWjDVE2B5uU67jCB
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>
tudor