TudorandClaude Opus 4.8
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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
110 lines
3.3 KiB
Python
110 lines
3.3 KiB
Python
"""_national_averages_payload reads persisted marts (computed at import
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time) — it must never aggregate the dataframe. Both marts hold OFFICIAL
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DfE figures, so a missing KS4 mart yields an empty secondary series —
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never a computed stand-in the UI would mislabel as official."""
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import numpy as np
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import pandas as pd
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import pytest
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LATEST = 202425
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def _df():
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return pd.DataFrame(
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[
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dict(year=202324, attainment_8_score=40.0, rwm_expected_pct=np.nan),
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dict(year=LATEST, attainment_8_score=50.0, rwm_expected_pct=np.nan),
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dict(year=LATEST, attainment_8_score=30.0, rwm_expected_pct=np.nan),
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dict(year=LATEST, attainment_8_score=np.nan, rwm_expected_pct=80.0),
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]
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)
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class _Ks2Row:
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year = LATEST
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rwm_expected_pct = 62.1
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gps_expected_pct = 72.0
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# Per-subject higher-standard nationals — reading/maths reach the higher
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# standard, writing is teacher-assessed at greater depth (writing_gd_pct).
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reading_high_pct = 29.0
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writing_gd_pct = 13.0
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maths_high_pct = 24.0
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class _Ks4Row:
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year = LATEST
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attainment_8_score = 46.5
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progress_8_score = -0.02
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class _StubSession:
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"""Returns KS2 rows for the first query and KS4 rows for the second —
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mirroring the payload's query order."""
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def __init__(self):
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self.calls = 0
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def query(self, model):
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self._model = model.__name__
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return self
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def order_by(self, *a):
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return self
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def all(self):
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return [_Ks2Row()] if self._model == "Ks2NationalAverage" else [_Ks4Row()]
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def close(self):
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pass
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class _Ks4MissingSession(_StubSession):
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def all(self):
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if self._model == "Ks4NationalAverage":
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raise RuntimeError("relation does not exist")
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return [_Ks2Row()]
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def rollback(self):
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pass
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@pytest.fixture()
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def payload(monkeypatch):
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from backend import app as app_module
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from backend import database as database_module
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def _run(session_cls):
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monkeypatch.setattr(database_module, "SessionLocal", session_cls)
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return app_module._national_averages_payload(_df())
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return _run
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def test_ks4_averages_come_from_the_mart_not_the_dataframe(payload):
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body = payload(_StubSession)
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# Mart value (46.5), NOT the dataframe mean of (50+30)/2 = 40.0
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assert body["secondary"]["attainment_8_score"] == 46.5
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assert body["primary"]["rwm_expected_pct"] == 62.1
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assert body["by_year"][-1]["secondary"]["progress_8_score"] == -0.02
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def test_per_subject_higher_standard_nationals_are_surfaced(payload):
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# The SATs chart compares each bar to its own benchmark, so the per-subject
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# higher-standard / greater-depth nationals must reach the payload — not
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# only the combined rwm_high_pct.
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body = payload(_StubSession)
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assert body["primary"]["reading_high_pct"] == 29.0
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assert body["primary"]["writing_gd_pct"] == 13.0
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assert body["primary"]["maths_high_pct"] == 24.0
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def test_ks4_secondary_empty_when_mart_missing(payload):
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# No computed stand-in: the UI labels national figures as official DfE
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# data, so an empty mart must yield an empty secondary series.
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body = payload(_Ks4MissingSession)
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assert body["secondary"] == {}
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assert all(not e["secondary"] for e in body["by_year"])
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# The KS2 series is unaffected.
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assert body["primary"]["rwm_expected_pct"] == 62.1
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