86 lines
3.2 KiB
Python
86 lines
3.2 KiB
Python
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"""Tests for the place registry (spec 2026-08-21).
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The registry is built from the in-memory school DataFrame, so these build a
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small frame directly rather than touching a database.
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"""
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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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from backend.places import MIN_SCHOOLS, build_place_registry
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def _df(rows: list[dict]) -> pd.DataFrame:
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base = {
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"year": 202425, "ofsted_grade": 2.0, "ofsted_date": None,
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"rwm_expected_pct": 60.0, "attainment_8_score": np.nan,
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"phase": "Primary", "postcode": "AA1 1AA",
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}
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return pd.DataFrame([{**base, **r} for r in rows])
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def _town(n: int, town: str, la: str, start: int = 100000, **kw) -> list[dict]:
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"""`start` offsets the URNs so two calls can describe different schools —
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the Bedford case needs two authorities' worth of distinct URNs in one
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town."""
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return [
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{"urn": start + i, "school_name": f"{town} School {i}",
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"town": town, "local_authority": la, **kw}
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for i in range(n)
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]
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def test_town_clearing_the_threshold_is_published():
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reg = build_place_registry(_df(_town(MIN_SCHOOLS, "Brentwood", "Essex")))
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assert "town:brentwood" in reg
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assert reg["town:brentwood"].name == "Brentwood"
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assert len(reg["town:brentwood"].urns) == MIN_SCHOOLS
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def test_town_below_the_threshold_is_not_published():
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reg = build_place_registry(_df(_town(MIN_SCHOOLS - 1, "Crosby", "Sefton")))
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assert "town:crosby" not in reg
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def test_a_town_below_threshold_still_names_its_authority():
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# The route layer needs somewhere to 301 to.
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reg = build_place_registry(_df(
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_town(MIN_SCHOOLS - 1, "Crosby", "Sefton") + _town(MIN_SCHOOLS, "Bootle", "Sefton")))
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assert "authority:sefton" in reg
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def test_town_and_authority_of_the_same_name_are_separate_places():
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# 67 real collisions. Neither set contains the other: Bedford the town has
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# 104 schools, Bedford the authority 86, because postal towns cross
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# authority boundaries.
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rows = (_town(MIN_SCHOOLS, "Bedford", "Bedford")
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+ _town(MIN_SCHOOLS, "Bedford", "Central Bedfordshire", start=200000))
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reg = build_place_registry(_df(rows))
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town, authority = reg["town:bedford"], reg["authority:bedford"]
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assert set(town.urns) != set(authority.urns)
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assert len(town.urns) == MIN_SCHOOLS * 2 # both authorities' schools
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assert len(authority.urns) == MIN_SCHOOLS # only this authority's
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def test_schools_without_publishable_data_do_not_count_toward_the_threshold():
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rows = _town(MIN_SCHOOLS, "Ghosttown", "Nowhere")
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for r in rows:
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r["rwm_expected_pct"] = np.nan
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r["ofsted_grade"] = np.nan
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reg = build_place_registry(_df(rows))
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assert "town:ghosttown" not in reg
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def test_blank_town_is_ignored():
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rows = _town(MIN_SCHOOLS, "", "Essex")
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reg = build_place_registry(_df(rows))
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assert not any(k.startswith("town:") for k in reg)
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def test_a_school_is_counted_once_even_with_several_years_of_rows():
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rows = []
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for year in (202324, 202425):
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rows += [{**r, "year": year} for r in _town(MIN_SCHOOLS, "Beccles", "Suffolk")]
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reg = build_place_registry(_df(rows))
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assert len(reg["town:beccles"].urns) == MIN_SCHOOLS
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