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Author SHA1 Message Date
TudorandClaude Opus 5 0571d1c0ff fix(web): stop the sheet-open rule stealing .sectionNav's layout
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Review catch, and a bad one: the previous commit anchored its insertion on
`padding: 0.5rem 0.75rem;` and closed .sectionNav there. Everything that
followed in the rule — margin-bottom, box-shadow, display: flex, align-items,
gap — was orphaned into .sectionNavSheetOpen, which is only applied while the
mobile jump sheet is open.

So the sticky nav lost its flex layout, spacing and shadow in the closed
state, which is virtually every page view on every school detail page. A
site-wide regression introduced by a fix for one mobile menu.

Redone by anchoring on the complete rule, closing brace included, so nothing
can be orphaned. .sectionNav is now byte-identical to main and the diff is
purely additive; .sectionNavSheetOpen carries the z-index and nothing else.

The staging experiment that validated this fix set nav.style.zIndex = '1100'
with every other declaration intact, so it was always testing this version
rather than the broken one.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-09-22 20:43:58 +01:00
TudorandClaude Opus 5 180d6e9b3e fix(web): lift the jump sheet above the bottom tab bar
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On mobile the last item in "Jump to section" was painted over by the fixed
bottom tab bar and could not be tapped. Reported as nearby schools missing
from the menu; it was there, underneath the bar.

The sticky nav sets `position: sticky` with `z-index: 10`, which makes it a
stacking context. The sheet's own `z-index: 1600` therefore orders it only
inside that context — against the tab bar (z-index 1000) the nav's 10 is what
counts, so the bar wins. Verified on staging: every menu item returns itself
from elementFromPoint except the last, which returns the tab bar.

Latent rather than new. With five sections the list stopped just above the bar;
"Nearby schools" made six, and the sixth is the first to reach it. Any section
added later would have done the same.

Lifted only while the sheet is open, and only to 1100 — above the bar, below
the comparison toast (2000), the fullscreen map (5000) and the info popover
(9999). The backdrop rises with it, so tapping over the bar now dismisses the
sheet instead of navigating away.

The journey asks what a thumb asks: for each item, whether it is the topmost
element at its own centre. A bounding-box check cannot see this — the item is
in the viewport and the right size, just underneath something. Confirmed to
fail against current staging, naming "Nearby schools", before the fix.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-09-22 20:35:25 +01:00
tudor 80f405123e Merge pull request 'test(e2e): wait for the carousel's smooth scroll to settle before measuring it' (#153) from fix/nearby-journey-waits-for-smooth-scroll into main
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Reviewed-on: #153
2026-09-22 14:55:16 +00:00
TudorandClaude Opus 5 077aca6008 test(e2e): wait for the carousel's smooth scroll to settle before measuring it
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Second failure of the same assertion, and the first fix addressed a real but
different problem. This is the one that was actually producing the number.

The arrows scroll with `behavior: 'smooth'`. The test polled for "has it moved
at all" — satisfied 50ms in, at 13px of a 1300px journey — then recorded the
offset, clicked, and recorded again. The row was still travelling throughout,
so the delta it measured was the tail of the arrow's animation, not the effect
of the selection. Hence a deterministic 659, roughly half of the 1317 this
school's row scrolls.

Measured against staging rather than reasoned about: the animation runs about
700ms, and the samples are in the helper's comment.

settledScrollLeft waits for two identical readings before trusting one. The app
was never at fault — driving staging by hand, the offset holds at 1317 across
the selection, exactly as intended.

Verified against staging both ways: main's version of this test fails there,
this version passes, along with all three mobile widths.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-09-22 15:13:32 +01:00
tudor 9dba5ff1ff Merge pull request 'test(e2e): fix the nearby-schools journey clicking a card it scrolled past' (#152) from fix/nearby-scroll-journey-clicks-offscreen-card into main
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Reviewed-on: #152
2026-09-22 13:50:25 +00:00
TudorandClaude Opus 5 f530a912bc test(e2e): stop the nearby-schools journey clicking a card it scrolled past
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The assertion that adding to compare does not reset the carousel failed on
staging: 537 → 2. The app was not at fault. Playwright scrolls a target into
view before clicking, and the test clicked the FIRST card's button after
paging the row to the end — so Playwright scrolled the container back to the
start, and the assertion measured that.

Reproduced on a static page with no React on it: a snap scroller at 615,
Playwright clicks the off-screen first card, scrollLeft becomes 2. Scroll-snap
was ruled out first — mandatory, proximity and no-snap all behave identically
when the button mutates in place.

Now clicks the last card's button, which is visible at the end of the travel,
and allows a few pixels for snap and sub-pixel adjustment while still failing
on a reset to the start. The property was never actually under test before;
it is now.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-09-22 14:19:00 +01:00
tudor 029fe8d8a6 Merge pull request 'fix: order nearby schools by distance, not by how alike they are' (#151) from fix/nearby-schools-order-by-distance into main
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Reviewed-on: #151
2026-09-22 13:09:38 +00:00
TudorandClaude Opus 5 cd1c5d1e1a docs: revise the spec to the design that survived staging
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The spec described the tier system as the design of record. It is gone, so
the document was describing something the code deliberately does not do.

The revision note and the "why not, having built it the other way first"
passage are kept rather than overwritten. The mistake is the instructive part:
treating a preference as a constraint inverted the ranking, and the stopping
rule added to prevent weak distant matches is what guaranteed six Catholic
schools and no community school down the road. A spec that quietly presents the
second design as the plan teaches nobody why the first one failed.

The mockup link is annotated as one revision behind rather than silently left
to look current.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-09-22 13:06:32 +01:00
TudorandClaude Opus 5 cd6a45bf7d refactor: rename similar → nearby, so the code says what the section does
The section ranks on distance and is headed "Other schools nearby", but every
identifier still called it "similar" — the exact drift that leaves a later
reader trusting a name over the behaviour.

Mechanical: files, the module, the payload key, the type, the components, the
prop. No behaviour change; the suites are unchanged in count and still green.
Free to do now because #150 has not merged, so the payload key rename needs no
lockstep deploy. Uses of "similar" that are ordinary English — progress
measures compared to similar pupils, and unrelated comments — are untouched.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-09-22 13:06:32 +01:00
TudorandClaude Opus 5 5c0ccc693d fix: order nearby schools by distance, not by how alike they are
Reported from staging: a Catholic primary showed six Catholic primaries, none
of them close enough to be a real option, and omitted the community school
down the road.

Three causes, compounding. Ranking put tier before distance, so a faith match
at 2.9 miles outranked a community school at 0.3. The ENOUGH=3 stopping rule —
added so a cap of six would not drag in weak distant matches — filled the row
from the best tier before it ever widened, which is what made every card
Catholic. And a 3-mile tier-1 radius is sane for a secondary and most of a city
for a primary, whose catchments are routinely under a mile.

The premise was backwards. For a parent, distance is a constraint and intake is
a preference; a school beyond a primary catchment is not a weaker option, it is
not an option. So distance now decides the order and nothing else does. The
hard filters are untouched — they were always where the defensibility lived.
Similarity survives as chips on the card: reported, so a reader applies their
own weighting, rather than ranked, so we apply ours for them.

Reach is capped per phase (primary 2, secondary 6, post-16 10) as a sanity
bound, not a target: ordering already handles density, so the cap only decides
what happens where an area is sparse. A primary with nothing inside two miles
now renders no section, which is the honest answer.

Deleted: the tier system, the stopping rule, the tier-dependent lede, the
`tier` field, the tier-3 fallback chip and its style. select_similar also stops
taking is_secondary — it reads the phase from the subject's own row, so no
caller can hand it one that disagrees with the data.

The heading is now "Other schools nearby". The hard filters still guarantee a
comparable set, but nothing ranks on likeness, so the heading no longer says it
does.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-09-22 13:06:32 +01:00
tudor 151cf4bc80 Merge pull request 'feat: similar schools nearby on the detail page' (#150) from feat/similar-schools-nearby into main
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Reviewed-on: #150
2026-09-22 05:53:06 +00:00
21 changed files with 617 additions and 439 deletions

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+12 -15
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@@ -41,7 +41,7 @@ from .data_loader import get_data_info as get_db_info
from . import flags
from .places import build_place_index, build_place_registry, places_for_urn
from .schemas import METRIC_DEFINITIONS, PHASE_GROUPS, RANKING_COLUMNS, SCHOOL_COLUMNS
from .similar_schools import is_secondary_phase, select_similar
from .nearby_schools import select_nearby
from .utils import clean_for_json, convert_to_native
# Values to exclude from filter dropdowns (empty strings, non-applicable labels)
@@ -266,25 +266,23 @@ def _places_payload(urn: int) -> list[dict]:
return payload
def _similar_schools_payload(urn: int, phase: str | None) -> list[dict]:
"""Nearby schools this page may offer as alternatives.
def _nearby_schools_payload(urn: int) -> list[dict]:
"""The nearest eligible schools this page may offer, closest first.
Phase and reach are read from the school's own row inside select_nearby,
so nothing here can hand it a phase that disagrees with the data.
Wrapped: a failure in selection must never 500 a page that is otherwise
complete, which is the posture get_supplementary_data already takes. The
section simply does not render.
"""
try:
# Decided in similar_schools, beside the PHASE_GROUPS bucket it selects
# from, so the two cannot drift. A substring test for "secondary" here
# would miss "16 plus" and hand a sixth-form college the primary bucket.
return select_similar(
load_latest_school_data(), int(urn), is_secondary_phase(phase)
)
return select_nearby(load_latest_school_data(), int(urn))
except Exception:
import logging
logging.getLogger(__name__).exception(
"Similar schools selection failed for urn=%s", urn
"Nearby schools selection failed for urn=%s", urn
)
return []
@@ -999,11 +997,10 @@ async def get_school_details(request: Request, urn: int):
# and authority both fall below the publish threshold has nowhere to
# point, and the page renders without the module.
"places": _places_payload(urn),
# Nearby schools of the same phase and a comparable intake. Always
# present on a build with this code; the frontend treats absent and
# empty identically, which is what lets the two images deploy
# independently.
"similar_schools": _similar_schools_payload(urn, latest.get("phase")),
# The nearest eligible schools, closest first. Always present on a
# build with this code; the frontend treats absent and empty
# identically, which is what lets the two images deploy independently.
"nearby_schools": _nearby_schools_payload(urn),
"yearly_data": clean_for_json(school_data),
# Supplementary data (null if not yet populated by Kestra)
"ofsted": supplementary.get("ofsted"),
@@ -1,17 +1,24 @@
"""Which nearby schools a detail page may offer as alternatives.
Two kinds of rule, and they are not interchangeable.
HARD FILTERS decide eligibility, and encode claims the section is not allowed
to make. A selective school is not an alternative to a non-selective one, a
special school is not comparable to a mainstream one, and a Girls school is not
an option for a Boys school's reader. They never relax, at any distance, even
where that means the section does not render at all.
HARD FILTERS encode claims the section is not allowed to make. A selective
school is not an alternative to a non-selective one, a special school is not
comparable to a mainstream one, and a Girls school is not an option for a Boys
school's reader. These never relax, at any distance, even where that means the
section does not render at all.
DISTANCE decides the order, and nothing else does.
SOFT PREFERENCES describe how closely an intake resembles this school's. They
relax in tiers, and every card reports the tier that actually took it so the
page can say what is shared rather than implying more. They relax only far
enough to reach a usable set, never far enough to fill the last of the slots.
An earlier version ranked by intake similarity first and used distance only as
a tiebreak. That put a Catholic school 2.9 miles away above the community
school 0.3 miles down the road, and — because the row filled from the best tier
before widening — filled all six slots with faith matches while omitting every
school a parent could actually walk to. For a primary, a school that far is not
a weaker option; it is not an option. Distance is a constraint and intake is a
preference, and the ranking now says so.
Similarity survives as `shared`: what a candidate genuinely has in common with
this school, reported on its card, so a reader applies their own weighting
instead of having ours applied for them.
Pure functions over a DataFrame: no I/O, no FastAPI, no database.
"""
@@ -25,19 +32,21 @@ import pandas as pd
from .schemas import PHASE_GROUPS
# A cap, not a quota: the section shows everything that qualified at the tiers
# it used, up to this many. Three fit the row; the rest are behind the arrows.
# Three fit the row; the rest are behind the carousel arrows.
MAX_SCHOOLS = 6
# Tiers stop relaxing once this many have been found. Without it, a cap of six
# would reliably drag in tier-3 schools ten miles away to fill a row that three
# good matches had already earned.
ENOUGH = 3
MINIMUM = 2
# (tier, radius in miles). Faith relaxes before gender: a faith mismatch
# changes the character of a school, while a gender mismatch can mean the
# school is not available to this reader's child at all.
TIERS: tuple[tuple[int, float], ...] = ((1, 3.0), (2, 5.0), (3, 10.0))
# How far the section will reach, in miles, when nothing closer exists.
#
# A sanity bound rather than a target: ordering by distance already handles
# density, so a school in a dense area fills all six slots inside a mile and
# never sees this. It decides one thing — what happens where the area is
# sparse — and the answer differs by phase because catchments do. Primary
# catchments are routinely under a mile; beyond two, a primary is not a weaker
# option but not an option, and no section is the honest answer.
PRIMARY_RADIUS_MILES = 2.0
SECONDARY_RADIUS_MILES = 6.0
POST16_RADIUS_MILES = 10.0
EARTH_RADIUS_MILES = 3958.8
@@ -80,15 +89,6 @@ def genders_compatible(a: str | None, b: str | None) -> bool:
return not (left in single and right in single and left != right)
def phase_label(phase: str | None) -> str:
text = (phase or "").strip()
if not text:
return "School"
if text.lower() == "all-through":
return "All-through school"
return f"{text.capitalize()} school"
def is_secondary_phase(phase: str | None) -> bool:
"""Whether this phase takes the secondary side: secondary group membership,
minus all-through.
@@ -107,6 +107,13 @@ def is_secondary_phase(phase: str | None) -> bool:
return text != "all-through" and text in PHASE_GROUPS["secondary"]
def radius_miles(phase: str | None) -> float:
"""How far this phase's section will reach when nothing closer exists."""
if (phase or "").strip().lower() == "16 plus":
return POST16_RADIUS_MILES
return SECONDARY_RADIUS_MILES if is_secondary_phase(phase) else PRIMARY_RADIUS_MILES
def _phase_group(is_secondary: bool) -> set[str]:
return PHASE_GROUPS["secondary" if is_secondary else "primary"]
@@ -144,26 +151,45 @@ def _mask(series: pd.Series, predicate) -> pd.Series:
return pd.Series([predicate(value) for value in series], index=series.index, dtype=bool)
def _chips(subject: pd.Series, candidate: pd.Series, tier: int, is_secondary: bool) -> list[str]:
if tier >= 3:
return [phase_label(candidate.get("phase"))]
def _shared(subject: pd.Series, candidate: pd.Series, is_secondary: bool) -> list[str]:
"""What this candidate genuinely has in common with the subject.
Empty is a real answer, and renders no chips at all. A card claiming a
shared characteristic it does not have would be worse than a bare one —
and since these no longer affect the order, an empty list costs the school
nothing but its place in the row, which distance already decided.
"""
shared: list[str] = []
gender = str(subject.get("gender") or "").strip()
if gender and str(candidate.get("gender") or "").strip().lower() == gender.lower():
shared.append(gender)
chips = [str(subject.get("gender") or "").strip()]
if is_secondary:
policy = (candidate.get("admissions_policy") or "").strip()
if policy and policy.lower() not in {"not applicable", "unknown"}:
chips.append(policy)
if tier == 1:
chips.append(faith_label(candidate.get("religious_denomination")))
return [chip for chip in chips if chip]
policy = str(candidate.get("admissions_policy") or "").strip()
subject_policy = str(subject.get("admissions_policy") or "").strip()
if (
policy
and policy.lower() == subject_policy.lower()
and policy.lower() not in {"not applicable", "unknown"}
):
shared.append(policy)
if faith_key(candidate.get("religious_denomination")) == faith_key(
subject.get("religious_denomination")
):
shared.append(faith_label(candidate.get("religious_denomination")))
return shared
def select_similar(frame: pd.DataFrame, urn: int, is_secondary: bool) -> list[dict]:
"""Up to MAX_SCHOOLS nearby schools this page may offer, or [] below MINIMUM.
def select_nearby(frame: pd.DataFrame, urn: int) -> list[dict]:
"""The nearest eligible schools, closest first — at most MAX_SCHOOLS, and
none at all below MINIMUM.
Selected by tier, displayed by distance: the tier decides which schools
earn a slot, and the render order is then closest-first, because "nearby"
is the promise in the heading.
The phase is read from the subject's own row rather than passed in, so a
caller cannot hand this a phase that disagrees with the data it selects
from.
"""
subject_rows = frame[frame["urn"] == urn]
if subject_rows.empty:
@@ -174,6 +200,9 @@ def select_similar(frame: pd.DataFrame, urn: int, is_secondary: bool) -> list[di
if lat is None or lon is None:
return []
phase = subject.get("phase")
is_secondary = is_secondary_phase(phase)
reach = radius_miles(phase)
metric_key = "attainment_8_score" if is_secondary else "rwm_expected_pct"
candidates = frame[frame["urn"] != urn].copy()
@@ -208,42 +237,12 @@ def select_similar(frame: pd.DataFrame, urn: int, is_secondary: bool) -> list[di
lat, lon, candidates["latitude"].values, candidates["longitude"].values
).round(1)
# ── Soft preferences, in tiers ──────────────────────────────────────
subject_faith = faith_key(subject.get("religious_denomination"))
subject_gender_key = (subject_gender or "").strip().lower()
same_gender = candidates["gender"].fillna("").str.strip().str.lower() == subject_gender_key
same_faith = _mask(
candidates["religious_denomination"], lambda d: faith_key(d) == subject_faith
)
tier_masks = {
1: same_gender & same_faith,
2: same_gender,
3: pd.Series(True, index=candidates.index),
}
# Descend the tiers only until the set reaches ENOUGH. The tier that gets
# there is the last one opened, and the remaining slots up to MAX_SCHOOLS
# are filled from the tiers already used — never by widening again.
picked: dict[int, tuple[int, pd.Series]] = {}
for tier, radius in TIERS:
within = candidates[tier_masks[tier] & (candidates["distance_miles"] <= radius)]
for _, row in within.sort_values("distance_miles").iterrows():
candidate_urn = int(row["urn"])
if candidate_urn in picked:
continue
picked[candidate_urn] = (tier, row)
if len(picked) >= MAX_SCHOOLS:
break
if len(picked) >= ENOUGH:
break
if len(picked) < MINIMUM:
# ── Nearest first, and nothing else has a say ───────────────────────
within = candidates[candidates["distance_miles"] <= reach]
if len(within) < MINIMUM:
return []
selected = sorted(
picked.values(), key=lambda pair: float(pair[1]["distance_miles"])
)[:MAX_SCHOOLS]
selected = within.sort_values(["distance_miles", "urn"]).head(MAX_SCHOOLS)
return [
{
"urn": int(row["urn"]),
@@ -251,11 +250,10 @@ def select_similar(frame: pd.DataFrame, urn: int, is_secondary: bool) -> list[di
"distance_miles": float(row["distance_miles"]),
"school_type": _native(row.get("school_type")),
"age_range": _native(row.get("age_range")),
"shared": _chips(subject, row, tier, is_secondary),
"tier": tier,
"shared": _shared(subject, row, is_secondary),
"metric_value": _native(row.get(metric_key)),
"metric_key": metric_key,
"metric_year": _native(row.get("year")),
}
for tier, row in selected
for _, row in selected.iterrows()
]
+1 -1
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@@ -536,7 +536,7 @@ RANKING_COLUMNS = [
# include. All-through schools appear in both primary and secondary results,
# which is why this is a set per phase rather than a single string comparison.
#
# Lives here rather than in app.py because similar_schools.py needs it too, and
# Lives here rather than in app.py because nearby_schools.py needs it too, and
# importing app from there would be a cycle.
PHASE_GROUPS: dict[str, set[str]] = {
"primary": {"primary", "middle deemed primary", "all-through"},
@@ -1,19 +1,26 @@
"""Selection rules for the "similar schools nearby" section.
"""Selection rules for the nearby-schools section.
The hard filters encode claims the section is not allowed to make — that a
Hard filters encode claims the section is not allowed to make — that a
selective school is an alternative to a non-selective one, that a special
school is comparable to a mainstream one, or that a Girls school is an option
for a Boys school's reader. They never relax. The soft preferences describe
how close the intake is, and they do — but only far enough to reach a usable
set, never far enough to fill the last of the six slots.
for a Boys school's reader. They decide who is eligible.
Distance decides the order, and nothing else does. An earlier version ranked by
intake similarity first, which put a Catholic school 2.9 miles away above the
community school 0.3 miles down the road — for a primary, a school that far is
not a weaker option, it is not an option. Similarity is now reported on the
card and never reorders the row.
"""
import numpy as np
import pandas as pd
from backend.similar_schools import is_secondary_phase, select_similar
from backend.nearby_schools import (
is_secondary_phase,
radius_miles,
select_nearby,
)
# Roughly 0.7 miles apart in latitude at this longitude.
BASE_LAT, BASE_LON = 51.5000, -0.1000
@@ -48,16 +55,68 @@ def _at(miles):
return BASE_LAT + miles / 69.0
def test_returns_nearest_same_phase_schools():
# ---------------------------------------------------------------------------
# Order: distance, and only distance
# ---------------------------------------------------------------------------
def test_returns_nearest_first():
frame = _frame(
_row(100001, "Subject"),
_row(100002, "Near", latitude=_at(0.5)),
_row(100003, "Mid", latitude=_at(1.0)),
_row(100004, "Far", latitude=_at(2.0)),
_row(100002, "Mid", latitude=_at(1.0)),
_row(100003, "Near", latitude=_at(0.4)),
_row(100004, "Far", latitude=_at(1.8)),
)
result = select_similar(frame, 100001, is_secondary=False)
assert [s["urn"] for s in result] == [100002, 100003, 100004]
assert result[0]["distance_miles"] == 0.5
result = select_nearby(frame, 100001)
assert [s["urn"] for s in result] == [100003, 100002, 100004]
assert result[0]["distance_miles"] == 0.4
def test_a_faith_match_never_outranks_a_closer_school():
"""The reported defect. A Catholic primary surrounded by Catholic primaries
showed six of them and omitted the community school down the road."""
frame = _frame(
_row(100001, "St Jude's RC Primary", religious_denomination="Roman Catholic"),
_row(100002, "Elm Grove Primary", religious_denomination="None", latitude=_at(0.3)),
_row(100003, "Holy Cross RC", religious_denomination="Roman Catholic", latitude=_at(0.8)),
_row(100004, "Sacred Heart RC", religious_denomination="Roman Catholic", latitude=_at(1.2)),
_row(100005, "St Peter's RC", religious_denomination="Roman Catholic", latitude=_at(1.6)),
)
result = select_nearby(frame, 100001)
assert result[0]["urn"] == 100002, "the nearest school leads, whatever its intake"
assert [s["distance_miles"] for s in result] == sorted(s["distance_miles"] for s in result)
def test_the_nearest_eligible_school_is_always_shown():
"""Whatever else changes, a section titled "nearby" cannot omit the nearest
school while listing one four times further away."""
frame = _frame(
_row(100001, "Subject", gender="Boys", religious_denomination="Roman Catholic"),
_row(100002, "Nearest", gender="Mixed", religious_denomination="None", latitude=_at(0.2)),
*[
_row(100010 + n, f"Match {n}", gender="Boys",
religious_denomination="Roman Catholic", latitude=_at(0.9 + n * 0.1))
for n in range(6)
],
)
assert select_nearby(frame, 100001)[0]["urn"] == 100002
def test_caps_at_six_taking_the_nearest():
frame = _frame(
_row(100001, "Subject"),
*[_row(100010 + n, f"Peer {n}", latitude=_at(0.1 * (n + 1))) for n in range(7)],
)
result = select_nearby(frame, 100001)
assert len(result) == 6
assert 100016 not in {s["urn"] for s in result}, "the seventh-nearest is the one dropped"
def test_fewer_than_two_matches_returns_empty():
frame = _frame(
_row(100001, "Subject"),
_row(100002, "Only neighbour", latitude=_at(0.5)),
)
assert select_nearby(frame, 100001) == []
def test_excludes_the_subject_school():
@@ -66,19 +125,66 @@ def test_excludes_the_subject_school():
_row(100002, "A", latitude=_at(0.5)),
_row(100003, "B", latitude=_at(0.6)),
)
assert 100001 not in {s["urn"] for s in select_similar(frame, 100001, is_secondary=False)}
assert 100001 not in {s["urn"] for s in select_nearby(frame, 100001)}
def test_a_school_is_never_listed_twice():
frame = _frame(
_row(100001, "Subject"),
_row(100002, "A", latitude=_at(0.5)),
_row(100003, "B", latitude=_at(0.6)),
)
result = select_nearby(frame, 100001)
assert len(result) == len({s["urn"] for s in result})
# ---------------------------------------------------------------------------
# Reach: a sanity bound, not a target
# ---------------------------------------------------------------------------
def test_primary_does_not_reach_past_two_miles():
frame = _frame(
_row(100001, "Subject"),
_row(100002, "Just inside", latitude=_at(1.9)),
_row(100003, "Just outside", latitude=_at(2.4)),
_row(100004, "Miles away", latitude=_at(4.0)),
)
# One inside the cap is below the minimum, so nothing renders at all —
# a primary with nothing within two miles has no nearby schools.
assert select_nearby(frame, 100001) == []
def test_secondary_reaches_further_than_primary():
frame = _frame(
_row(100001, "Subject", phase="Secondary"),
_row(100002, "A", phase="Secondary", latitude=_at(3.0)),
_row(100003, "B", phase="Secondary", latitude=_at(5.5)),
)
assert {s["urn"] for s in select_nearby(frame, 100001)} == {100002, 100003}
def test_the_cap_follows_the_phase():
assert radius_miles("Primary") == 2.0
assert radius_miles("Middle deemed primary") == 2.0
assert radius_miles("All-through") == 2.0
assert radius_miles("Secondary") == 6.0
assert radius_miles("Middle deemed secondary") == 6.0
# Post-16 is the phase people travel furthest for.
assert radius_miles("16 plus") == 10.0
# ---------------------------------------------------------------------------
# Hard filters: eligibility, never order
# ---------------------------------------------------------------------------
def test_selective_never_meets_non_selective():
frame = _frame(
_row(100001, "Grammar", phase="Secondary", admissions_policy="Selective"),
_row(100002, "Comp A", phase="Secondary", admissions_policy="Non-selective", latitude=_at(0.5)),
_row(100003, "Comp B", phase="Secondary", admissions_policy="Non-selective", latitude=_at(0.6)),
)
assert select_similar(frame, 100001, is_secondary=True) == []
reverse = select_similar(frame, 100002, is_secondary=True)
assert 100001 not in {s["urn"] for s in reverse}
assert select_nearby(frame, 100001) == []
assert 100001 not in {s["urn"] for s in select_nearby(frame, 100002)}
def test_special_schools_match_only_each_other():
@@ -87,8 +193,8 @@ def test_special_schools_match_only_each_other():
_row(100002, "Mainstream A", latitude=_at(0.5)),
_row(100003, "Mainstream B", latitude=_at(0.6)),
)
assert select_similar(frame, 100001, is_secondary=False) == []
assert select_similar(frame, 100002, is_secondary=False) == []
assert select_nearby(frame, 100001) == []
assert select_nearby(frame, 100002) == []
def test_boys_never_meets_girls():
@@ -98,7 +204,7 @@ def test_boys_never_meets_girls():
_row(100003, "Mixed School", gender="Mixed", latitude=_at(0.6)),
_row(100004, "Another Mixed", gender="Mixed", latitude=_at(0.7)),
)
urns = {s["urn"] for s in select_similar(frame, 100001, is_secondary=False)}
urns = {s["urn"] for s in select_nearby(frame, 100001)}
assert 100002 not in urns
assert urns == {100003, 100004}
@@ -111,80 +217,7 @@ def test_closed_schools_and_missing_coordinates_are_dropped():
_row(100004, "Good A", latitude=_at(0.6)),
_row(100005, "Good B", latitude=_at(0.7)),
)
assert {s["urn"] for s in select_similar(frame, 100001, is_secondary=False)} == {100004, 100005}
def test_tiers_relax_faith_before_gender():
frame = _frame(
_row(100001, "Subject", gender="Boys", religious_denomination="Roman Catholic"),
# Tier 1: same gender and same faith.
_row(100002, "Tier one", gender="Boys", religious_denomination="Roman Catholic", latitude=_at(2.0)),
# Tier 2: same gender, different faith — closer, but a weaker match.
_row(100003, "Tier two", gender="Boys", religious_denomination="None", latitude=_at(0.5)),
# Tier 3: mixed gender, different faith.
_row(100004, "Tier three", gender="Mixed", religious_denomination="None", latitude=_at(0.6)),
)
result = select_similar(frame, 100001, is_secondary=False)
tier_by_urn = {s["urn"]: s["tier"] for s in result}
assert tier_by_urn == {100002: 1, 100003: 2, 100004: 3}
# Selected by tier, displayed by distance.
assert [s["urn"] for s in result] == [100003, 100004, 100002]
def test_caps_at_six_taking_the_nearest():
frame = _frame(
_row(100001, "Subject"),
*[_row(100010 + n, f"Peer {n}", latitude=_at(0.1 * (n + 1))) for n in range(7)],
)
result = select_similar(frame, 100001, is_secondary=False)
assert len(result) == 6
# The seventh-nearest is the one dropped, not an arbitrary one.
assert 100016 not in {s["urn"] for s in result}
def test_tiers_stop_once_enough_are_found():
"""Four tier-1 matches are a usable set, so tier 2 is never opened — even
though it holds a school that is closer than any of them."""
frame = _frame(
_row(100001, "Subject", religious_denomination="Roman Catholic"),
_row(100002, "RC one", religious_denomination="Roman Catholic", latitude=_at(0.5)),
_row(100003, "RC two", religious_denomination="Roman Catholic", latitude=_at(0.6)),
_row(100004, "RC three", religious_denomination="Roman Catholic", latitude=_at(0.7)),
_row(100005, "RC four", religious_denomination="Roman Catholic", latitude=_at(0.8)),
# Closer than every one of them, but only a tier-2 match.
_row(100006, "Secular and nearer", religious_denomination="None", latitude=_at(0.2)),
)
result = select_similar(frame, 100001, is_secondary=False)
assert 100006 not in {s["urn"] for s in result}
assert len(result) == 4
assert all(s["tier"] == 1 for s in result)
def test_a_school_is_never_taken_twice():
frame = _frame(
_row(100001, "Subject"),
_row(100002, "A", latitude=_at(0.5)),
_row(100003, "B", latitude=_at(0.6)),
)
result = select_similar(frame, 100001, is_secondary=False)
assert len(result) == len({s["urn"] for s in result})
def test_fewer_than_two_matches_returns_empty():
frame = _frame(
_row(100001, "Subject"),
_row(100002, "Only neighbour", latitude=_at(0.5)),
)
assert select_similar(frame, 100001, is_secondary=False) == []
def test_beyond_the_widest_radius_is_not_offered():
frame = _frame(
_row(100001, "Subject"),
_row(100002, "A", latitude=_at(11.0)),
_row(100003, "B", latitude=_at(12.0)),
)
assert select_similar(frame, 100001, is_secondary=False) == []
assert {s["urn"] for s in select_nearby(frame, 100001)} == {100004, 100005}
def test_all_through_is_offered_on_both_phase_sides():
@@ -193,14 +226,14 @@ def test_all_through_is_offered_on_both_phase_sides():
_row(100002, "All through", phase="All-through", latitude=_at(0.5)),
_row(100003, "Primary peer", phase="Primary", latitude=_at(0.6)),
)
assert 100002 in {s["urn"] for s in select_similar(frame, 100001, is_secondary=False)}
assert 100002 in {s["urn"] for s in select_nearby(frame, 100001)}
secondary = _frame(
_row(100010, "Secondary subject", phase="Secondary"),
_row(100002, "All through", phase="All-through", latitude=_at(0.5)),
_row(100011, "Secondary peer", phase="Secondary", latitude=_at(0.6)),
)
assert 100002 in {s["urn"] for s in select_similar(secondary, 100010, is_secondary=True)}
assert 100002 in {s["urn"] for s in select_nearby(secondary, 100010)}
def test_sixteen_plus_is_matched_against_secondary_not_primary():
@@ -212,11 +245,10 @@ def test_sixteen_plus_is_matched_against_secondary_not_primary():
_row(100001, "Sixth Form College", phase="16 plus", age_range="16-19"),
_row(100002, "Nearby Secondary", phase="Secondary", latitude=_at(0.5),
attainment_8_score=52.0),
_row(100003, "Nearby College", phase="16 plus", latitude=_at(0.6),
attainment_8_score=np.nan),
_row(100003, "Nearby College", phase="16 plus", latitude=_at(0.6)),
_row(100004, "Nearby Primary", phase="Primary", latitude=_at(0.1)),
)
result = select_similar(frame, 100001, is_secondary=is_secondary_phase("16 plus"))
result = select_nearby(frame, 100001)
urns = {s["urn"] for s in result}
assert 100004 not in urns, "a primary school is not a peer for a sixth form"
assert urns == {100002, 100003}
@@ -224,18 +256,20 @@ def test_sixteen_plus_is_matched_against_secondary_not_primary():
def test_is_secondary_phase_agrees_with_the_phase_groups_it_selects_from():
"""The two must not drift: whatever this calls secondary decides which
PHASE_GROUPS bucket the candidates come from."""
for phase in ("Secondary", "Middle deemed secondary", "16 plus"):
assert is_secondary_phase(phase) is True, phase
for phase in ("Primary", "Middle deemed primary", "Nursery", "", None):
assert is_secondary_phase(phase) is False, phase
# In PHASE_GROUPS an all-through school is on both sides, but it renders
# with the primary template, and the metric follows the template.
# with the primary template, and the metric follows the phase side.
assert is_secondary_phase("All-through") is False
def test_chips_state_only_what_the_tier_earned():
# ---------------------------------------------------------------------------
# What the card reports
# ---------------------------------------------------------------------------
def test_shared_lists_only_what_is_actually_shared():
frame = _frame(
_row(100001, "Subject", phase="Secondary", gender="Mixed",
religious_denomination="None", admissions_policy="Non-selective"),
@@ -244,28 +278,47 @@ def test_chips_state_only_what_the_tier_earned():
_row(100003, "Faith differs", phase="Secondary", gender="Mixed",
religious_denomination="Church of England", admissions_policy="Non-selective", latitude=_at(0.6)),
)
by_urn = {s["urn"]: s for s in select_similar(frame, 100001, is_secondary=True)}
by_urn = {s["urn"]: s for s in select_nearby(frame, 100001)}
assert by_urn[100002]["shared"] == ["Mixed", "Non-selective", "No religious character"]
assert by_urn[100003]["shared"] == ["Mixed", "Non-selective"]
def test_tier_three_chip_is_the_plain_phase():
def test_a_shared_faith_is_named():
frame = _frame(
_row(100001, "Subject", gender="Boys"),
_row(100002, "A", gender="Mixed", latitude=_at(0.5)),
_row(100003, "B", gender="Mixed", latitude=_at(0.6)),
_row(100001, "Subject", religious_denomination="Roman Catholic"),
_row(100002, "Also RC", religious_denomination="Roman Catholic", latitude=_at(0.4)),
_row(100003, "Secular", religious_denomination="None", latitude=_at(0.5)),
)
result = select_similar(frame, 100001, is_secondary=False)
assert all(s["shared"] == ["Primary school"] for s in result)
by_urn = {s["urn"]: s for s in select_nearby(frame, 100001)}
assert "Roman Catholic" in by_urn[100002]["shared"]
assert by_urn[100003]["shared"] == ["Mixed"]
def test_metric_follows_the_template_not_the_neighbour():
def test_shared_is_empty_when_nothing_is_shared():
frame = _frame(
_row(100001, "Subject", gender="Boys", religious_denomination="Roman Catholic"),
_row(100002, "A", gender="Mixed", religious_denomination="None", latitude=_at(0.4)),
_row(100003, "B", gender="Mixed", religious_denomination="Church of England", latitude=_at(0.5)),
)
assert all(s["shared"] == [] for s in select_nearby(frame, 100001))
def test_no_tier_is_reported_because_there_are_no_tiers():
frame = _frame(
_row(100001, "Subject"),
_row(100002, "A", latitude=_at(0.4)),
_row(100003, "B", latitude=_at(0.5)),
)
assert all("tier" not in s for s in select_nearby(frame, 100001))
def test_metric_follows_the_phase_side_not_the_neighbour():
frame = _frame(
_row(100001, "Subject", phase="Secondary", attainment_8_score=50.0),
_row(100002, "A", phase="Secondary", attainment_8_score=52.8, latitude=_at(0.5)),
_row(100003, "B", phase="Secondary", attainment_8_score=np.nan, latitude=_at(0.6)),
)
by_urn = {s["urn"]: s for s in select_similar(frame, 100001, is_secondary=True)}
by_urn = {s["urn"]: s for s in select_nearby(frame, 100001)}
assert by_urn[100002]["metric_key"] == "attainment_8_score"
assert by_urn[100002]["metric_value"] == 52.8
assert by_urn[100002]["metric_year"] == 202425
@@ -278,7 +331,7 @@ def test_values_are_json_safe_native_types():
_row(100002, "A", latitude=_at(0.5)),
_row(100003, "B", latitude=_at(0.6)),
)
for school in select_similar(frame, 100001, is_secondary=False):
for school in select_nearby(frame, 100001):
assert isinstance(school["urn"], int)
assert isinstance(school["distance_miles"], float)
assert not isinstance(school["metric_value"], np.generic)
@@ -310,10 +363,10 @@ def client(monkeypatch):
return TestClient(app_module.app, raise_server_exceptions=False)
def test_detail_payload_carries_similar_schools(client):
def test_detail_payload_carries_nearby_schools(client):
resp = client.get("/api/schools/100001")
assert resp.status_code == 200, resp.text
similar = resp.json()["similar_schools"]
similar = resp.json()["nearby_schools"]
assert [s["school_name"] for s in similar] == ["Neighbour A", "Neighbour B"]
assert similar[0]["metric_key"] == "rwm_expected_pct"
@@ -324,7 +377,7 @@ def test_a_failure_in_selection_does_not_break_the_page(client, monkeypatch):
def _explode(*args, **kwargs):
raise ValueError("selection blew up")
monkeypatch.setattr(app_module, "select_similar", _explode)
monkeypatch.setattr(app_module, "select_nearby", _explode)
resp = client.get("/api/schools/100001")
assert resp.status_code == 200, resp.text
assert resp.json()["similar_schools"] == []
assert resp.json()["nearby_schools"] == []
+6 -6
View File
@@ -73,12 +73,12 @@ There is no SWR dependency. Leaflet maps are loaded through dynamic wrappers;
Chart.js renders performance and comparison charts.
`components/school/` contains detail sections, with section decisions and data
preparation in `lib/schoolSections.ts`. The similar-schools section is selected
in `backend/similar_schools.py` — hard filters that never relax (phase,
provision, selectivity, gender) and soft preferences that do (religious
character, then gender exactness) — and served on `/api/schools/{urn}`. Its
rules are presentation logic, deliberately kept out of `marts.*` so they can be
tuned by deploy rather than by pipeline run. `lib/types.ts` contains manually maintained
preparation in `lib/schoolSections.ts`. The nearby-schools section is selected in
`backend/nearby_schools.py` — hard filters decide eligibility (phase, provision,
selectivity, gender) and distance alone decides the order, capped per phase —
and served on `/api/schools/{urn}`. Its rules are presentation logic,
deliberately kept out of `marts.*` so they can be tuned by deploy rather than by
pipeline run. `lib/types.ts` contains manually maintained
API types. `payload-types.ts` and the Payload import map are generated artifacts.
## Publication and caching today
@@ -1,9 +1,16 @@
# Similar Schools Nearby — Design
# Other Schools Nearby — Design
**Date:** 2026-09-21
**Status:** awaiting review
**Date:** 2026-09-21, revised 2026-09-22
**Status:** revised after staging review
**Scope:** school detail pages, both phase templates
> **Revision, 2026-09-22.** The first build ranked by intake similarity and used
> distance as a tiebreak. On staging a Catholic primary showed six Catholic
> primaries, none of them close enough to be a real option, and omitted the
> community school down the road. Distance now decides the order and nothing
> else does; the tier system is gone. The reasoning is kept below rather than
> quietly overwritten, because the mistake is the instructive part.
## Goal
Give a school detail page an answer to the question every reader arrives with
@@ -15,7 +22,8 @@ school. This section adds that edge — up to six nearby schools of the same pha
and a comparable intake, three at a time in a carousel, each a crawlable link and
each addable to the comparison basket in one click.
Mockup, with all three tier states live in both themes:
Mockup, in both themes (drawn against the original tiered design, so its ledes
and chip fallbacks are one revision behind the copy specified below):
<https://claude.ai/artifact/168KdUMcfkUeGWW2FGjuec>
Source of the same page in the repo: `mockups/similar-schools-nearby.html`.
@@ -37,19 +45,23 @@ alternative, and there are three ways that claim goes wrong:
3. A **single-sex** school of the opposite sex. Not a weak match — not an option
at all.
So the design separates two kinds of rule, and never confuses them:
So the design separates two kinds of fact, and never confuses them:
- **Hard filters** encode the claims above. They are never relaxed, at any
distance, even if that means the section does not render.
- **Soft preferences** describe how closely the intake resembles this school's.
They relax in tiers, and the card's own text always states what survived.
- **Hard filters** encode the claims above. They decide eligibility, and are
never relaxed at any distance, even if that means the section does not render.
- **Shared characteristics** — gender, religious character, selectivity —
describe how closely an intake resembles this school's. They are *reported on
the card and never ranked on*, so the reader weighs them rather than having
them weighed for them.
Everything below follows from that split.
Everything below follows from that split. The revision at the top of this
document is what happens when the second kind is treated as the first.
## Selection algorithm
A backend helper, `_similar_schools_payload(urn)` in `backend/app.py`, modelled
on the existing `_places_payload(urn)` and operating on the cached
A backend helper, `_nearby_schools_payload(urn)` in `backend/app.py`, modelled
on the existing `_places_payload(urn)` and delegating to
`backend/nearby_schools.select_nearby(frame, urn)`, which operates on the cached
`load_latest_school_data()` frame — one row per URN, already carrying
`latitude`, `longitude`, `phase`, `gender`, `religious_denomination`,
`admissions_policy`, `school_type` and `status`.
@@ -77,64 +89,63 @@ school the frontend treats as special for benchmarking but the backend treats as
mainstream for matching would be dropped from its own England comparison and
then offered as a peer to a mainstream school on the next page along.
**Up to six cards, three visible.** Six is a cap, not a quota: the section shows
every school that qualifies at the tiers it used, up to six. Three fit the row,
and the rest are reached with the carousel arrows. Two is the minimum that
renders at all.
**Up to six cards, three visible.** Three fit the row; the rest are reached with
the carousel arrows. Two is the minimum that renders at all.
### Soft preferences, relaxed in tiers
### Order: distance, and nothing else
| Tier | Additionally requires | Radius |
|---|---|---|
| 1 | exact gender equality **and** same religious character | 3 miles |
| 2 | exact gender equality | 5 miles |
| 3 | nothing beyond the hard filters | 10 miles |
The nearest eligible schools, closest first. Similarity does not enter the
ranking at any point.
**Tiers relax to reach a usable set, never to fill the last slots.**
**Why not, having built it the other way first.** The original design ranked by
tiers — same gender and faith within 3 miles, then same gender within 5, then
anything within 10 — and used distance only to order the result. Two things
followed, and both showed up on the first Catholic primary anyone looked at:
Work down the tiers until the schools found so far reach three. Call the tier
that got there T. The section then shows up to six schools drawn from tiers 1
to T, nearest first — and does not open tier T+1 merely because six slots are
not yet full.
- A faith match at 2.9 miles outranked a community school at 0.3 miles. For a
primary, whose catchment is routinely under a mile, the far school is not a
weaker option; it is not an option.
- Because the row filled from the best tier before widening, three Catholic
schools within 3 miles were enough to fill all six slots with Catholic
schools. The stopping rule that produced this had been added to prevent the
*opposite* failure — padding a row with weak distant matches — and made this
one certain.
Worked through:
The premise was backwards. **Distance is a constraint and intake is a
preference.** A parent cannot act on a school outside their reach however well
it matches, and they are perfectly capable of noticing a shared denomination
for themselves if we show it to them. So similarity moved from the ranking to
the card: `shared` reports what a school genuinely has in common, and the reader
applies their own weighting.
| Qualifying | T | Shown |
|---|---|---|
| 14 at tier 1 | 1 | the 6 nearest tier-1 schools |
| 4 at tier 1 | 1 | all 4 — tier 2 is never opened |
| 2 at tier 1, 7 more at tier 2 | 2 | the 6 nearest of those 9 |
| 2 at tier 1, 1 at tier 2 | 2 | all 3 |
| 2 across all three tiers | 3 | both, since 2 is the minimum |
The hard filters above were always where the defensibility lived. They are
untouched.
Without that stopping rule, a cap of six would reliably drag in tier-3 schools
ten miles away to fill a row that three good matches had already earned. The old
cap of three hid this; six exposes it, which is why the rule is stated rather
than left to the loop.
### Reach: a sanity bound, not a target
**Faith relaxes before gender.** A faith mismatch changes the character of a
school; a gender mismatch can mean the school is not available to the reader's
child at all. Ordering them the other way would fill the section with schools
that cannot be applied to.
| Phase | Reach |
|---|---|
| Primary, middle deemed primary, all-through | 2 miles |
| Secondary, middle deemed secondary | 6 miles |
| 16 plus | 10 miles |
### Two decisions that are easy to get wrong later
Ordering by distance already handles density — a school in inner London fills
all six slots inside a mile and never approaches the cap. The cap decides one
thing: what happens where the area is sparse. It differs by phase because
catchments do, and because people travel furthest for post-16.
**Selected by tier, displayed by distance.** Tier decides *which* three schools
earn a slot. The rendered order is then distance ascending, because "nearby" is
the promise in the heading and a reader scanning the row reads the first card as
the closest. A tier-2 school at 0.4 miles therefore appears above a tier-1
school at 2.9 miles, and the chips explain the difference in match quality.
**A primary with nothing inside two miles renders no section**, and that is the
intended answer rather than a gap. The alternative is a section headed "nearby"
listing a school four miles from a five-year-old.
**Past the sixth school, the rest are dropped without a count.** In inner
London dozens clear tier 1, and a parent there will notice three is not the
neighbourhood — hence six. Beyond that the section does not try to be the list:
`NearbyPlaces` sits directly beneath and already leads to the place pages, which
are built for browsing a full set and which the school page exists to feed.
**Past the sixth school, the rest are dropped without a count.** The section
does not try to be the list: `NearbyPlaces` sits directly beneath and already
leads to the place pages, which are built for browsing a full set and which the
school page exists to feed.
**Fewer than two results renders nothing.** Not an empty state, not a single
lonely card, not padding with schools that failed the hard filters. The section
is absent, the nav item is absent, and the page is unchanged from today. A page
with one weak match is better off without the section than with it.
lonely card. The section is absent, the nav item is absent, and the page is
unchanged from before it existed.
### Distance
@@ -157,14 +168,13 @@ reader to assume otherwise.
| `distance_miles` | one decimal place |
| `school_type` | GIAS type, translated, for the card's meta line |
| `age_range` | for the meta line |
| `shared` | the chip strings the tier actually justifies — see below |
| `tier` | 1, 2 or 3 — drives the lede's wording and the chip styling |
| `shared` | what this school genuinely shares with the subject; may be empty |
| `metric_value` | the phase-appropriate headline figure, or null |
| `metric_key` | `rwm_expected_pct` or `attainment_8_score` — see below |
| `metric_year` | the year the figure is from |
The metric follows the phase side the school was *matched* on, not the
neighbour's own phase, so a row of cards never mixes two scales. The secondary
The metric follows the subject school's phase side, not the neighbour's own
phase, so a row of cards never mixes two scales. The secondary
side uses `attainment_8_score`; the primary side uses `rwm_expected_pct`. Where
the neighbour has no value for that key, the card reads "Not published" rather
than falling back to the other key.
@@ -183,12 +193,6 @@ correctly — against secondaries. The section therefore takes its lede noun fro
the school's own phase rather than from its template, or it would print "Other
primary schools near <sixth form college>" above a row of secondaries.
`tier` is carried explicitly rather than inferred from the contents of
`shared`, because the frontend needs it for two separate decisions — whether the
lede may claim a similar intake, and whether a chip renders as a brand-tinted
fill or a muted outline — and inferring it from chip count would couple those
decisions to the copy.
Up to six rows of roughly 130 bytes each. It rides in the existing detail payload
rather than a new endpoint because the page already makes exactly one server
fetch for its data, and `/school/[slug]` regenerates at most weekly
@@ -199,9 +203,11 @@ week.
frontend treats absent and empty identically, which is what allowed
`NearbyPlaces` to ship without a lockstep deploy of the two images.
`shared` is computed on the backend beside the tier that produced it, not
re-derived on the frontend. Deriving it twice is how a card comes to claim a
match the selection did not actually make.
`shared` is computed on the backend, beside the data it is derived from, not
re-derived on the frontend. Deriving it twice is how a card comes to claim
something the selection never established. An empty list is a real answer and
renders no chips: a bare card costs a school nothing but the likeness it does
not have, since the order was already settled by distance.
## Frontend
@@ -308,16 +314,21 @@ shows.
## Copy, and what the section is allowed to claim
**The lede tracks the deepest tier shown.** At tiers 1–2 it reads "Other primary
schools near X, with a similar intake." Where any card came from tier 3 it drops
"with a similar intake", because for at least one of the cards that is not what
was matched. Six cards make this more likely to fire than three did, which is
correct: a wider net is exactly when the claim needs dropping.
**The lede never claims an intake.** It reads "Other primary schools near X." —
one sentence, no variants. The earlier version varied the wording by tier, which
only existed to soften a claim the section should not have been making.
**Chips state only what is shared.** A tier-2 card carries fewer chips rather
than a chip it has not earned; a tier-3 card falls back to the plain phase name,
styled as a muted outline rather than a brand-tinted fill so the difference is
visible at a glance.
**The heading is "Other schools nearby", not "Similar schools nearby".** The
hard filters do guarantee a comparable set — same phase, same selectivity,
mainstream never beside special — but nothing ranks on likeness, so the heading
does not say it does. The nav item reads "Nearby schools" and the section id is
`nearby`.
**Chips state only what is shared, and may be absent entirely.** A card with
nothing in common renders no chip row rather than falling back to a filler.
Since chips no longer affect the order, an empty one costs that school nothing
except a claim it cannot support — and a Catholic parent scanning the row still
spots "Roman Catholic" on the card that carries it, and weighs it themselves.
**The neighbour's metric carries no valence colour.** Green and terracotta are
reserved site-wide for comparison against the England average. Colouring a
@@ -364,9 +375,11 @@ synthetic frame rather than live marts:
- a special school returns only special schools; a mainstream school returns none
- a Boys school never returns a Girls school; Mixed matches both
- closed schools and schools without coordinates are never returned
- tier relaxation fills in order, and a school taken at tier 1 is not repeated
- tiers stop relaxing once three are found: four tier-1 matches never open tier 2
- results are ordered by distance ascending, always
- a faith match never outranks a closer school (the staging defect, pinned)
- the nearest eligible school is always present
- more than six qualifying schools returns the six nearest
- reach is capped per phase, and a primary beyond two miles returns `[]`
- an all-through school is offered on both phase sides
- a `16 plus` school is matched against secondaries and colleges, never primaries
- `is_secondary_phase` and `PHASE_GROUPS` agree on every GIAS phase value
@@ -376,7 +389,8 @@ synthetic frame rather than live marts:
**Frontend**, in `nextjs-app/__tests__`:
- the section renders nothing for absent, empty and single-row inputs
- the lede drops "with a similar intake" when any card is tier 3
- the lede never claims a similar intake
- an empty `shared` renders no chips rather than a filler
- a null metric renders "Not published"
- the nav item appears only alongside the section
- every card is in the DOM, including the ones scrolled out of view
@@ -408,10 +422,11 @@ journeys are confirmed on the post-merge staging run.
- Autoplay, dots, or an infinite loop on the carousel. It is a short list a
reader scans deliberately, not a banner competing for attention, and a row
that moves on its own is a row that moves while someone is reading it.
- Statistical neighbours on deprivation, size or cohort profile. If the tiers
prove too coarse, that is the trigger to move this computation into a dbt mart
— `_similar_schools_payload` is a deliberate seam for exactly that swap.
- Statistical neighbours on deprivation, size or cohort profile. If plain
distance proves too blunt, that is the trigger to move this computation into a
dbt mart — `select_nearby` is a deliberate seam for exactly that swap.
- Precomputing neighbours in `marts.*`. Rejected for now: a new mart is inert
until Airflow runs, so the feature would ship dark, and every tuning change to
the tiers would become a pipeline round-trip instead of a deploy.
the rules would become a pipeline round-trip instead of a deploy. The revision
at the top of this document is the argument for keeping that loop short.
- Any change to `/api/compare`, the compare page, or the comparison basket.
+93 -13
View File
@@ -1,4 +1,4 @@
import { test, expect, Page } from '@playwright/test';
import { test, expect, Locator, Page } from '@playwright/test';
/**
* Journey tests for SchoolCompare, run against the staging environment as the
@@ -19,6 +19,31 @@ function schoolLinks(page: Page) {
return page.locator('a[href^="/school/"]');
}
/**
* A scroll offset that has stopped moving.
*
* The carousel arrows scroll with `behavior: 'smooth'`, so a reading taken
* straight after a click lands mid-animation. Measured against staging: the
* animation runs ~700ms, and a poll for "has it moved at all" is satisfied
* 50ms in, at 13px of a 1300px journey. A test that then records an offset,
* does something, and records again is measuring the tail of the arrow's
* animation rather than the effect of whatever it did in between.
*
* Two identical readings in a row is the cheapest sound definition of settled.
*/
async function settledScrollLeft(scroller: Locator): Promise<number> {
let previous = -1;
await expect
.poll(async () => {
const current = await scroller.evaluate((node: HTMLElement) => Math.round(node.scrollLeft));
const settled = current === previous;
previous = current;
return settled;
}, { timeout: 10_000 })
.toBe(true);
return previous;
}
/**
* Two URNs guaranteed to be pure-primary (same phase). The compare page's
* phase tabs split all-through schools (which carry KS4 data) onto the
@@ -2736,7 +2761,7 @@ test('the content sitemap lists the about page and is advertised in robots', asy
});
/**
* Similar schools nearby.
* Other schools nearby.
*
* The section is absent by design where fewer than two schools qualify, and the
* arrows are absent where three cards fit, so this asserts each part of the
@@ -2746,12 +2771,12 @@ test('the content sitemap lists the about page and is advertised in robots', asy
* which jsdom cannot measure because it has no layout, and the scroll position
* surviving a selection, which is DOM state rather than React state.
*/
test('similar schools link on to other schools and into compare', async ({ page }) => {
test('nearby schools link on to other schools and into compare', async ({ page }) => {
await searchByName(page, 'Primary');
await schoolLinks(page).first().click();
await page.waitForURL(/\/school\//);
const section = page.locator('#similar');
const section = page.locator('#nearby');
if ((await section.count()) === 0) {
test.skip(true, 'No qualifying similar schools for this school');
}
@@ -2776,24 +2801,79 @@ test('similar schools link on to other schools and into compare', async ({ page
await expect(back).toBeDisabled();
await forward.click();
await expect
.poll(() => scroller.evaluate((node: HTMLElement) => node.scrollLeft))
.toBeGreaterThan(8);
expect(await settledScrollLeft(scroller)).toBeGreaterThan(8);
await expect(back).toBeEnabled();
}
// The compare hand-off, and the row must not jump back to the start when the
// footer re-renders underneath it.
const offsetBefore = await scroller.evaluate((node: HTMLElement) => node.scrollLeft);
await section.getByRole('button', { name: /Add to compare/ }).first().click();
//
// Click the LAST card's button, not the first. Playwright scrolls a target
// into view before clicking it, so clicking card one while the row is paged
// to the end scrolls the container back to the start — and the assertion
// below then measures Playwright's own scrolling rather than the app's.
// That is what this test did on its first staging run: 537 → 2, reproduced
// afterwards on a static page with no React on it at all.
//
// A few pixels of snap or sub-pixel adjustment are fine; a reset to the
// start is not, which is the whole point of the check.
const offsetBefore = await settledScrollLeft(scroller);
await section.getByRole('button', { name: /Add to compare/ }).last().click();
await expect(
section.getByRole('button', { name: /Added to compare/ }).first(),
).toBeVisible();
expect(await scroller.evaluate((node: HTMLElement) => node.scrollLeft)).toBe(offsetBefore);
const offsetAfter = await settledScrollLeft(scroller);
expect(Math.abs(offsetAfter - offsetBefore)).toBeLessThanOrEqual(8);
});
/**
* The section at MOBILE.md's three reference widths.
* Every section in the mobile jump sheet can actually be reached.
*
* The sheet is a fixed bottom sheet, and the app has a fixed bottom tab bar.
* `position: sticky` with a z-index on the sticky nav makes it a stacking
* context, so the sheet's own z-index orders it only within that context —
* against the tab bar, the nav's value is what counts. The last item in the
* sheet was therefore painted over and untappable as soon as the list grew
* long enough to reach the bar, which adding "Nearby schools" is what did.
*
* Bounding boxes are not enough to catch this: the item is in the viewport and
* the right size, it is simply underneath something. So this asks the question
* a thumb asks — what is on top at this point.
*/
test('every section in the mobile jump sheet is tappable, not under the tab bar', async ({ page }) => {
await page.setViewportSize({ width: 390, height: 844 });
await searchByName(page, 'Primary');
await schoolLinks(page).first().click();
await page.waitForURL(/\/school\//);
// Scroll down so the sticky nav is docked and the sheet has somewhere to open.
await page.evaluate(() => window.scrollTo({ top: 1200 }));
// Two controls carry aria-haspopup: the mobile "Section" button and the
// desktop "All" one, which is display:none here but still in the DOM.
await page.locator('[aria-haspopup="menu"]:visible').click();
const sheet = page.locator('[role="menu"]');
await expect(sheet).toBeVisible();
const covered = await sheet.evaluate((panel: HTMLElement) =>
Array.from(panel.querySelectorAll('[role="menuitem"]'))
.map((el) => {
const box = el.getBoundingClientRect();
const hit = document.elementFromPoint(
Math.round(box.left + box.width / 2),
Math.round(box.top + box.height / 2),
);
return { label: (el as HTMLElement).innerText.trim().replace(/\s+/g, ' '), reachable: !!(hit && hit.closest('[role="menuitem"]')) };
})
.filter((item) => !item.reachable)
.map((item) => item.label),
);
expect(covered).toEqual([]);
});
/**
* The nearby-schools section at MOBILE.md's three reference widths.
*
* MOBILE.md asks for exactly this check and records that it was not written
* because "Playwright isn't currently in the project dependency set". That is
@@ -2801,13 +2881,13 @@ test('similar schools link on to other schools and into compare', async ({ page
* to the page this feature touches.
*/
for (const width of [360, 390, 430]) {
test(`similar schools survives a ${width}px viewport`, async ({ page }) => {
test(`nearby schools survives a ${width}px viewport`, async ({ page }) => {
await page.setViewportSize({ width, height: 800 });
await searchByName(page, 'Primary');
await schoolLinks(page).first().click();
await page.waitForURL(/\/school\//);
const section = page.locator('#similar');
const section = page.locator('#nearby');
if ((await section.count()) === 0) {
test.skip(true, 'No qualifying similar schools for this school');
}
@@ -1,32 +1,32 @@
/**
* The section's job is to be honest about what it matched. These tests pin the
* ways it could lie: rendering below the minimum, claiming a similar intake at
* tier 3, showing a missing figure as a number, or hiding a card behind an
* arrow where a crawler cannot reach it.
* The section's job is to be honest about what it is showing. These tests pin
* the ways it could mislead: rendering below the minimum, claiming a likeness
* it does not rank on, showing a missing figure as a number, or hiding a card
* behind an arrow where a crawler cannot reach it.
*/
import { render, screen } from '@testing-library/react';
import {
nearbyNoun,
SimilarSchoolsSection,
shouldRenderSimilar,
} from '@/components/school/SimilarSchoolsSection';
import type { SimilarSchool } from '@/lib/types';
NearbySchoolsSection,
shouldRenderNearby,
} from '@/components/school/NearbySchoolsSection';
import type { NearbySchool } from '@/lib/types';
jest.mock('@/components/school/AddToCompareButton', () => ({
AddToCompareButton: ({ school }: { school: SimilarSchool }) => (
AddToCompareButton: ({ school }: { school: NearbySchool }) => (
<button type="button">Add {school.school_name} to compare</button>
),
}));
jest.mock('@/components/school/SimilarSchoolsCompareBar', () => ({
SimilarSchoolsCompareBar: ({ thisUrn }: { thisUrn: number }) => (
jest.mock('@/components/school/NearbySchoolsCompareBar', () => ({
NearbySchoolsCompareBar: ({ thisUrn }: { thisUrn: number }) => (
<div data-testid="compare-bar">bar for {thisUrn}</div>
),
}));
function school(overrides: Partial<SimilarSchool> = {}): SimilarSchool {
function school(overrides: Partial<NearbySchool> = {}): NearbySchool {
return {
urn: 100002,
school_name: 'Willow Lane Primary School',
@@ -34,7 +34,6 @@ function school(overrides: Partial<SimilarSchool> = {}): SimilarSchool {
school_type: 'Community school',
age_range: '4-11',
shared: ['Mixed', 'No religious character'],
tier: 1,
metric_value: 74,
metric_key: 'rwm_expected_pct',
metric_year: 202425,
@@ -42,14 +41,14 @@ function school(overrides: Partial<SimilarSchool> = {}): SimilarSchool {
};
}
function renderSection(similar: SimilarSchool[]) {
function renderSection(nearby: NearbySchool[]) {
return render(
<SimilarSchoolsSection
<NearbySchoolsSection
urn={100001}
schoolName="Meadowbrook Primary School"
phase="Primary"
thisMetricValue={72}
similar={similar}
nearby={nearby}
/>,
);
}
@@ -61,11 +60,11 @@ describe('render gates', () => {
['empty', []],
['a single school', [school()]],
])('renders nothing for %s', (_label, value) => {
expect(shouldRenderSimilar(value as SimilarSchool[] | null | undefined)).toBe(false);
expect(shouldRenderNearby(value as NearbySchool[] | null | undefined)).toBe(false);
});
it('renders for two or more schools', () => {
expect(shouldRenderSimilar([school(), school({ urn: 100003 })])).toBe(true);
expect(shouldRenderNearby([school(), school({ urn: 100003 })])).toBe(true);
});
it('returns null rather than an empty shell below the minimum', () => {
@@ -74,15 +73,36 @@ describe('render gates', () => {
});
});
describe('the claim the lede makes', () => {
it('claims a similar intake when every card is tier 1 or 2', () => {
renderSection([school({ tier: 1 }), school({ urn: 100003, tier: 2 })]);
expect(screen.getByText(/with a similar intake/i)).toBeInTheDocument();
describe('what the section claims', () => {
it('never claims a similar intake, because it does not rank on one', () => {
renderSection([school(), school({ urn: 100003, shared: [] })]);
expect(screen.queryByText(/similar intake/i)).not.toBeInTheDocument();
});
it('drops the claim when any card is tier 3', () => {
renderSection([school({ tier: 1 }), school({ urn: 100003, tier: 3, shared: ['Primary school'] })]);
expect(screen.queryByText(/with a similar intake/i)).not.toBeInTheDocument();
it('is headed "Other schools nearby", not "similar"', () => {
renderSection([school(), school({ urn: 100003 })]);
expect(screen.getByRole('heading', { name: 'Other schools nearby' })).toBeInTheDocument();
});
it('shows chips for what is shared', () => {
renderSection([school({ shared: ['Mixed', 'Roman Catholic'] }), school({ urn: 100003 })]);
expect(screen.getAllByText('Roman Catholic').length).toBe(1);
});
it('shows no chips at all when nothing is shared, rather than inventing one', () => {
const { container } = render(
<NearbySchoolsSection
urn={100001}
schoolName="Meadowbrook Primary School"
phase="Primary"
thisMetricValue={72}
nearby={[school({ shared: [] }), school({ urn: 100003, shared: [] })]}
/>,
);
// The card still carries its distance, name, type and figure — just no
// claim of likeness.
expect(container.querySelectorAll('li ul').length).toBe(0);
expect(screen.getAllByText(/miles away/).length).toBe(2);
});
});
@@ -104,12 +124,12 @@ describe('what the lede calls the set', () => {
it('never calls a sixth form college\'s neighbours primary schools', () => {
render(
<SimilarSchoolsSection
<NearbySchoolsSection
urn={100001}
schoolName="Barnet Sixth Form College"
phase="16 plus"
thisMetricValue={null}
similar={[school(), school({ urn: 100003 })]}
nearby={[school(), school({ urn: 100003 })]}
/>,
);
expect(screen.getByText(/Other schools and colleges near Barnet Sixth Form College/)).toBeInTheDocument();
+10 -10
View File
@@ -173,7 +173,7 @@ describe('buildSecondaryNavItems', () => {
});
});
describe('the similar-schools nav item', () => {
describe('the nearby-schools nav item', () => {
const navInput = {
ofsted: null, admissions: null, admissionDistance: null,
hasLocation: true, yearlyDataLength: 1,
@@ -182,29 +182,29 @@ describe('the similar-schools nav item', () => {
it('appears on both templates when the section renders', () => {
const primary = computeSchoolFlags(primaryFixture);
const secondary = computeSecondaryFlags(secondaryFixture);
const input = { ...navInput, hasSimilarSchools: true };
const input = { ...navInput, hasNearbySchools: true };
expect(buildNavItems(primary, input).map((i) => i.id)).toContain('similar');
expect(buildSecondaryNavItems(secondary, input).map((i) => i.id)).toContain('similar');
expect(buildNavItems(primary, input).map((i) => i.id)).toContain('nearby');
expect(buildSecondaryNavItems(secondary, input).map((i) => i.id)).toContain('nearby');
});
it('is absent when the section does not render', () => {
const primary = computeSchoolFlags(primaryFixture);
const secondary = computeSecondaryFlags(secondaryFixture);
const input = { ...navInput, hasSimilarSchools: false };
const input = { ...navInput, hasNearbySchools: false };
expect(buildNavItems(primary, input).map((i) => i.id)).not.toContain('similar');
expect(buildSecondaryNavItems(secondary, input).map((i) => i.id)).not.toContain('similar');
expect(buildNavItems(primary, input).map((i) => i.id)).not.toContain('nearby');
expect(buildSecondaryNavItems(secondary, input).map((i) => i.id)).not.toContain('nearby');
});
it('is absent when nothing says either way', () => {
const primary = computeSchoolFlags(primaryFixture);
expect(buildNavItems(primary, navInput).map((i) => i.id)).not.toContain('similar');
expect(buildNavItems(primary, navInput).map((i) => i.id)).not.toContain('nearby');
});
it('comes last, because the section renders last', () => {
const primary = computeSchoolFlags(primaryFixture);
const ids = buildNavItems(primary, { ...navInput, hasSimilarSchools: true }).map((i) => i.id);
expect(ids[ids.length - 1]).toBe('similar');
const ids = buildNavItems(primary, { ...navInput, hasNearbySchools: true }).map((i) => i.id);
expect(ids[ids.length - 1]).toBe('nearby');
});
});
@@ -8,7 +8,7 @@ import { APIFetchError, fetchSchoolDetails, fetchSchools, fetchNationalAverages
import { notFound, redirect } from 'next/navigation';
import { SchoolDetailShell } from '@/components/school/SchoolDetailShell';
import { NearbyPlaces } from '@/components/school/NearbyPlaces';
import { shouldRenderSimilar } from '@/components/school/SimilarSchoolsSection';
import { shouldRenderNearby } from '@/components/school/NearbySchoolsSection';
import { schoolBreadcrumbJsonLd, type SchoolPlace } from '@/lib/jsonld';
import { PrimarySchoolSections } from '@/components/school/PrimarySchoolSections';
import { SecondarySchoolSections } from '@/components/school/SecondarySchoolSections';
@@ -157,7 +157,7 @@ export default async function SchoolPage({ params }: SchoolPageProps) {
// lockstep deploy of the two images.
const places: SchoolPlace[] = data.places ?? [];
// Absent on an older API build, exactly like `places` above.
const similarSchools = data.similar_schools ?? [];
const nearbySchools = data.nearby_schools ?? [];
// Redirect bare URN to canonical slug URL
const canonicalSlug = schoolUrl(urn, school_info.school_name).replace('/school/', '');
@@ -189,7 +189,7 @@ export default async function SchoolPage({ params }: SchoolPageProps) {
admissions: admissions ?? null,
admissionDistance: admission_distance ?? null,
hasLocation: school_info.latitude != null && school_info.longitude != null,
hasSimilarSchools: shouldRenderSimilar(similarSchools),
hasNearbySchools: shouldRenderNearby(nearbySchools),
yearlyDataLength: yearly_data.length,
};
const primaryNavItems = buildNavItems(primaryFlags, navInput);
@@ -266,7 +266,7 @@ export default async function SchoolPage({ params }: SchoolPageProps) {
finance={finance ?? null}
nationalAvg={nationalAvg}
destinations={destinations ?? null}
similarSchools={similarSchools}
nearbySchools={nearbySchools}
flags={secondaryFlags}
/>
</SchoolDetailShell>
@@ -289,7 +289,7 @@ export default async function SchoolPage({ params }: SchoolPageProps) {
deprivation={deprivation ?? null}
finance={finance ?? null}
nationalAvg={nationalAvg}
similarSchools={similarSchools}
nearbySchools={nearbySchools}
flags={primaryFlags}
/>
</SchoolDetailShell>
@@ -9,10 +9,10 @@
*/
import { useComparisonContext } from '@/context/ComparisonContext';
import type { School, SimilarSchool } from '@/lib/types';
import styles from './SimilarSchools.module.css';
import type { School, NearbySchool } from '@/lib/types';
import styles from './NearbySchools.module.css';
export function AddToCompareButton({ school }: { school: SimilarSchool }) {
export function AddToCompareButton({ school }: { school: NearbySchool }) {
const { addSchool, removeSchool, selectedSchools } = useComparisonContext();
const selected = selectedSchools.some((s) => s.urn === school.urn);
@@ -39,7 +39,6 @@
.shared { display: flex; flex-wrap: wrap; gap: 0.35rem; list-style: none; margin: 0 0 0.85rem; padding: 0; }
.chip { font-size: 0.72rem; line-height: 1.4; padding: 0.25rem 0.5rem; border-radius: 999px; background: var(--brand-bg); color: var(--brand); border: 1px solid transparent; }
.chipLoose { font-size: 0.72rem; line-height: 1.4; padding: 0.25rem 0.5rem; border-radius: 999px; background: transparent; color: var(--text-muted); border: 1px solid var(--border); }
.metric { margin-top: auto; padding-top: 0.8rem; border-top: 1px solid var(--border); }
/* No valence colour here, deliberately: green and terracotta mean "against the
@@ -14,7 +14,7 @@
*/
import { useCallback, useEffect, useRef, useState, type ReactNode } from 'react';
import styles from './SimilarSchools.module.css';
import styles from './NearbySchools.module.css';
/** Three cards fit the row, so fewer than four has nowhere to scroll to.
* Below 640px the arrows are not rendered at all — see the stylesheet. */
@@ -31,7 +31,7 @@ const VISIBLE = 3;
*/
const EDGE = 8;
export function SimilarSchoolsCarousel({
export function NearbySchoolsCarousel({
count,
labelledBy,
header,
@@ -11,15 +11,15 @@
import Link from 'next/link';
import { useComparisonContext } from '@/context/ComparisonContext';
import type { SimilarSchool } from '@/lib/types';
import styles from './SimilarSchools.module.css';
import type { NearbySchool } from '@/lib/types';
import styles from './NearbySchools.module.css';
export function SimilarSchoolsCompareBar({
export function NearbySchoolsCompareBar({
thisUrn,
candidates,
}: {
thisUrn: number;
candidates: SimilarSchool[];
candidates: NearbySchool[];
}) {
const { selectedSchools } = useComparisonContext();
@@ -1,12 +1,16 @@
/**
* SimilarSchoolsSection — nearby schools of the same phase and a comparable
* intake. Server component; only the carousel, the compare bar and the
* add-to-compare button are client-side.
* NearbySchoolsSection — the nearest eligible schools, closest first. Server
* component; only the carousel, the compare bar and the add-to-compare button
* are client-side.
*
* The section is allowed to say exactly what the backend matched and no more.
* The lede only claims a similar intake when no card came from tier 3, and a
* card's chips list what that school actually shares rather than a match it
* did not earn.
* "Other schools nearby", not "similar" ones: the order is distance and only
* distance. The hard filters upstream still guarantee the set is comparable —
* same phase, same selectivity, mainstream never beside special — but nothing
* here ranks by how alike two schools are, so the heading does not say it does.
*
* The chips report what a school shares, and may be absent entirely. That is
* information for the reader to weigh, not a verdict this section has already
* reached on their behalf.
*
* There is deliberately no "how these are chosen" panel: the method is already
* visible in the lede, the chips and the distances. The single caption line is
@@ -14,18 +18,18 @@
*/
import Link from 'next/link';
import type { SimilarSchool } from '@/lib/types';
import type { NearbySchool } from '@/lib/types';
import { schoolUrl } from '@/lib/utils';
import { AddToCompareButton } from './AddToCompareButton';
import { SimilarSchoolsCarousel } from './SimilarSchoolsCarousel';
import { SimilarSchoolsCompareBar } from './SimilarSchoolsCompareBar';
import { NearbySchoolsCarousel } from './NearbySchoolsCarousel';
import { NearbySchoolsCompareBar } from './NearbySchoolsCompareBar';
import { Section } from './sectionShared';
import styles from './SimilarSchools.module.css';
import styles from './NearbySchools.module.css';
const MINIMUM = 2;
export function shouldRenderSimilar(similar?: SimilarSchool[] | null): boolean {
return (similar?.length ?? 0) >= MINIMUM;
export function shouldRenderNearby(nearby?: NearbySchool[] | null): boolean {
return (nearby?.length ?? 0) >= MINIMUM;
}
/**
@@ -59,44 +63,39 @@ function formatMetric(value: number | null, key: string): string {
return key === 'attainment_8_score' ? value.toFixed(1) : `${Math.round(value)}%`;
}
export function SimilarSchoolsSection({
export function NearbySchoolsSection({
urn,
schoolName,
phase,
thisMetricValue,
similar,
nearby,
}: {
urn: number;
schoolName: string;
/** The school's own GIAS phase, not the template it renders with. */
phase: string | null | undefined;
thisMetricValue: number | null;
similar?: SimilarSchool[] | null;
nearby?: NearbySchool[] | null;
}) {
if (!shouldRenderSimilar(similar)) return null;
const schools = similar as SimilarSchool[];
if (!shouldRenderNearby(nearby)) return null;
const schools = nearby as NearbySchool[];
// One card matched on phase alone, so the section may not claim the set
// shares an intake with this school.
const loosest = Math.max(...schools.map((s) => s.tier));
const metricKey = schools[0].metric_key;
const noun = nearbyNoun(phase);
return (
<Section id="similar">
<SimilarSchoolsCarousel
<Section id="nearby">
<NearbySchoolsCarousel
count={schools.length}
labelledBy="similar-schools-heading"
labelledBy="nearby-schools-heading"
header={
<div>
<h2 id="similar-schools-heading" className={styles.heading}>
Similar schools nearby
<h2 id="nearby-schools-heading" className={styles.heading}>
Other schools nearby
</h2>
<p className={styles.lede}>
{loosest >= 3
? `Other ${noun} near ${schoolName}.`
: `Other ${noun} near ${schoolName}, with a similar intake.`}
</p>
<p className={styles.lede}>{`Other ${noun} near ${schoolName}.`}</p>
</div>
}
>
@@ -113,13 +112,13 @@ export function SimilarSchoolsSection({
.filter(Boolean)
.join(' · ')}
</p>
<ul className={styles.shared}>
{school.shared.map((label) => (
<li key={label} className={school.tier >= 3 ? styles.chipLoose : styles.chip}>
{label}
</li>
))}
</ul>
{school.shared.length > 0 && (
<ul className={styles.shared}>
{school.shared.map((label) => (
<li key={label} className={styles.chip}>{label}</li>
))}
</ul>
)}
<div className={styles.metric}>
<p
className={
@@ -138,9 +137,9 @@ export function SimilarSchoolsSection({
<AddToCompareButton school={school} />
</li>
))}
</SimilarSchoolsCarousel>
</NearbySchoolsCarousel>
<SimilarSchoolsCompareBar thisUrn={urn} candidates={schools} />
<NearbySchoolsCompareBar thisUrn={urn} candidates={schools} />
{/* The one caveat the cards cannot make on their own: a reader who takes
"0.6 miles away" for the walk has been misled, and nothing else here
@@ -14,7 +14,7 @@
import type {
School, SchoolResult, AbsenceData, OfstedInspection, SchoolCensus,
SchoolAdmissions, SchoolAdmissionDistance, SchoolDeprivation, SchoolFinance, NationalAverages,
SimilarSchool,
NearbySchool,
} from '@/lib/types';
import { ofstedLegacyAreas } from '@/lib/utils';
import type { SchoolFlags } from '@/lib/schoolSections';
@@ -27,7 +27,7 @@ import { HistorySection } from './HistorySection';
import { SchoolLifeSection } from './SchoolLifeSection';
import { LocalAreaSection } from './LocalAreaSection';
import { FinancesSection } from './FinancesSection';
import { SimilarSchoolsSection } from './SimilarSchoolsSection';
import { NearbySchoolsSection } from './NearbySchoolsSection';
export interface PrimarySchoolSectionsProps {
schoolInfo: School;
@@ -42,14 +42,14 @@ export interface PrimarySchoolSectionsProps {
finance: SchoolFinance | null;
nationalAvg: NationalAverages | null;
/** Nearby schools of a comparable intake. Absent on an older API build. */
similarSchools?: SimilarSchool[];
nearbySchools?: NearbySchool[];
flags: SchoolFlags;
}
export function PrimarySchoolSections({
schoolInfo, yearlyData, absenceData, ofsted, census,
admissions, admissionsHistory, admissionDistance,
deprivation, finance, nationalAvg, similarSchools, flags,
deprivation, finance, nationalAvg, nearbySchools, flags,
}: PrimarySchoolSectionsProps) {
const primaryAvg = nationalAvg?.primary ?? {};
const secondaryAvg = nationalAvg?.secondary ?? {};
@@ -152,12 +152,12 @@ export function PrimarySchoolSections({
{flags.hasFinance && finance && <FinancesSection finance={finance} />}
{/* Last: it is where the reader goes next, not part of this school. */}
<SimilarSchoolsSection
<NearbySchoolsSection
urn={schoolInfo.urn}
schoolName={schoolInfo.school_name}
phase={schoolInfo.phase}
thisMetricValue={flags.latestResults?.rwm_expected_pct ?? null}
similar={similarSchools}
nearby={nearbySchools}
/>
</>
);
@@ -283,6 +283,21 @@
}
/* `position: sticky` with a z-index makes .sectionNav a stacking context, so
the jump sheet's own z-index only orders it INSIDE that context. Against the
fixed bottom tab bar (Navigation.module.css, z-index 1000) what counts is
.sectionNav's 10 — which is why the sheet's last item was painted over, and
untappable, once the list grew long enough to reach the bar.
Lifted only while the sheet is open, and only to 1100: above the bar, below
the comparison toast (2000), the fullscreen map (5000) and the info popover
(9999). This rule carries the z-index and nothing else; every other
declaration belongs to .sectionNav in both states. */
.sectionNavSheetOpen {
z-index: 1100;
}
.sectionNavBack {
flex: none;
display: inline-flex;
@@ -344,7 +344,10 @@ export function SchoolDetailShell({
</header>
{/* Sticky Section Navigation — docks under the global header */}
<nav className={styles.sectionNav} aria-label="Page sections">
<nav
className={`${styles.sectionNav}${sectionsOpen ? ` ${styles.sectionNavSheetOpen}` : ''}`}
aria-label="Page sections"
>
<button onClick={scrollToTop} className={styles.sectionNavBack} aria-label="Back to top">
<span aria-hidden="true">↑</span>
<span className={styles.sectionNavBackLabel}>Top</span>
@@ -14,7 +14,7 @@
import type {
School, SchoolResult, AbsenceData, OfstedInspection, SchoolCensus,
SchoolAdmissions, SchoolAdmissionDistance, SchoolDeprivation, SchoolFinance, NationalAverages,
SchoolDestinations, SimilarSchool,
SchoolDestinations, NearbySchool,
} from '@/lib/types';
import { ofstedLegacyAreas } from '@/lib/utils';
import type { SecondaryFlags } from '@/lib/schoolSections';
@@ -27,7 +27,7 @@ import { DistanceSection } from './DistanceSection';
import { SecondaryHistorySection } from './SecondaryHistorySection';
import { WellbeingSection } from './WellbeingSection';
import { FinancesSection } from './FinancesSection';
import { SimilarSchoolsSection } from './SimilarSchoolsSection';
import { NearbySchoolsSection } from './NearbySchoolsSection';
import styles from './schoolSections.module.css';
export interface SecondarySchoolSectionsProps {
@@ -49,14 +49,14 @@ export interface SecondarySchoolSectionsProps {
nationalAvg: NationalAverages | null;
destinations: SchoolDestinations | null;
/** Nearby schools of a comparable intake. Absent on an older API build. */
similarSchools?: SimilarSchool[];
nearbySchools?: NearbySchool[];
flags: SecondaryFlags;
}
export function SecondarySchoolSections({
schoolInfo, yearlyData, ofsted, census,
admissions, admissionsHistory, admissionDistance,
deprivation, finance, nationalAvg, destinations, similarSchools, flags,
deprivation, finance, nationalAvg, destinations, nearbySchools, flags,
}: SecondarySchoolSectionsProps) {
const secondaryAvg = nationalAvg?.secondary ?? {};
@@ -146,12 +146,12 @@ export function SecondarySchoolSections({
)}
{/* Last: it is where the reader goes next, not part of this school. */}
<SimilarSchoolsSection
<NearbySchoolsSection
urn={schoolInfo.urn}
schoolName={schoolInfo.school_name}
phase={schoolInfo.phase}
thisMetricValue={flags.latestResults?.attainment_8_score ?? null}
similar={similarSchools}
nearby={nearbySchools}
/>
</div>
);
+6 -6
View File
@@ -128,10 +128,10 @@ export interface NavItemsInput {
* measure a postcode, so the nav must gate on them too or it will link to an
* anchor that was never rendered. */
hasLocation?: boolean;
/** Whether the similar-schools section will render. Optional for the same
/** Whether the nearby-schools section will render. Optional for the same
* reason hasLocation is: the nav must never link to an anchor that was not
* rendered, and absent has to mean "no section". */
hasSimilarSchools?: boolean;
hasNearbySchools?: boolean;
yearlyDataLength: number;
}
@@ -148,7 +148,7 @@ export function buildNavItems(
flags: SchoolFlags,
{
ofsted, admissions, admissionDistance, hasLocation,
hasSimilarSchools, yearlyDataLength,
hasNearbySchools, yearlyDataLength,
}: NavItemsInput,
): NavItem[] {
const navItems: NavItem[] = [];
@@ -169,7 +169,7 @@ export function buildNavItems(
if (flags.hasDeprivation) navItems.push({ id: 'local-area', label: 'Local Area' });
if (flags.hasFinance) navItems.push({ id: 'finances', label: 'Finances' });
// Last, because the section renders last — the scroll-spy reads this order.
if (hasSimilarSchools) navItems.push({ id: 'similar', label: 'Similar schools' });
if (hasNearbySchools) navItems.push({ id: 'nearby', label: 'Nearby schools' });
return navItems;
}
@@ -250,7 +250,7 @@ export function buildSecondaryNavItems(
flags: SecondaryFlags,
{
ofsted, admissions, admissionDistance, hasLocation,
hasSimilarSchools, yearlyDataLength,
hasNearbySchools, yearlyDataLength,
}: NavItemsInput,
): NavItem[] {
const navItems: NavItem[] = [];
@@ -270,6 +270,6 @@ export function buildSecondaryNavItems(
if (flags.hasWellbeing) navItems.push({ id: 'wellbeing', label: 'Wellbeing' });
if (flags.hasFinance) navItems.push({ id: 'finances', label: 'Finances' });
// Last, because the section renders last — the scroll-spy reads this order.
if (hasSimilarSchools) navItems.push({ id: 'similar', label: 'Similar schools' });
if (hasNearbySchools) navItems.push({ id: 'nearby', label: 'Nearby schools' });
return navItems;
}
+8 -9
View File
@@ -347,21 +347,20 @@ export interface SchoolsResponse {
}
/**
* A nearby school of the same phase and a comparable intake.
* A nearby school, from the nearest-first set the detail page shows.
*
* `tier` is carried explicitly rather than inferred from `shared`, because it
* drives two separate decisions — whether the lede may claim a similar intake,
* and whether a chip renders as a fill or a muted outline — and inferring it
* from chip count would couple those decisions to the copy.
* `shared` is what this school genuinely has in common with the one being
* viewed, and may be empty. It is reported, never ranked on: an earlier
* version ordered by it and buried the school down the road under faith
* matches three times further away.
*/
export interface SimilarSchool {
export interface NearbySchool {
urn: number;
school_name: string;
distance_miles: number;
school_type: string | null;
age_range: string | null;
shared: string[];
tier: number;
metric_value: number | null;
metric_key: string;
metric_year: number | null;
@@ -379,13 +378,13 @@ export interface SchoolDetailsResponse {
*/
places?: SchoolPlace[];
/**
* Up to six nearby schools of a comparable intake, nearest first.
* Up to six nearby eligible schools, nearest first.
*
* Optional for the same reason as `places`: a frontend deployed ahead of the
* API that serves this must render without it. Absent and empty mean the
* same thing here — no section.
*/
similar_schools?: SimilarSchool[];
nearby_schools?: NearbySchool[];
yearly_data: SchoolResult[];
absence_data: AbsenceData | null;
// Supplementary data (null until Kestra populates)