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