feat(places): registry of towns and authorities

Two namespaces because 67 town names collide with an authority name and
neither set contains the other — postal towns cross authority boundaries, so
Bedford the town holds 104 schools against the authority's 86.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_015mWQnpye9F299NVRCCSRvj

backend/tests/test_places.py

@@ -0,0 +1,86 @@
+"""Tests for the place registry (spec 2026-08-21).
+
+The registry is built from the in-memory school DataFrame, so these build a
+small frame directly rather than touching a database.
+"""
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from backend.places import MIN_SCHOOLS, build_place_registry
+
+
+def _df(rows: list[dict]) -> pd.DataFrame:
+    base = {
+        "year": 202425, "ofsted_grade": 2.0, "ofsted_date": None,
+        "rwm_expected_pct": 60.0, "attainment_8_score": np.nan,
+        "phase": "Primary", "postcode": "AA1 1AA",
+    }
+    return pd.DataFrame([{**base, **r} for r in rows])
+
+
+def _town(n: int, town: str, la: str, start: int = 100000, **kw) -> list[dict]:
+    """`start` offsets the URNs so two calls can describe different schools —
+    the Bedford case needs two authorities' worth of distinct URNs in one
+    town."""
+    return [
+        {"urn": start + i, "school_name": f"{town} School {i}",
+         "town": town, "local_authority": la, **kw}
+        for i in range(n)
+    ]
+
+
+def test_town_clearing_the_threshold_is_published():
+    reg = build_place_registry(_df(_town(MIN_SCHOOLS, "Brentwood", "Essex")))
+    assert "town:brentwood" in reg
+    assert reg["town:brentwood"].name == "Brentwood"
+    assert len(reg["town:brentwood"].urns) == MIN_SCHOOLS
+
+
+def test_town_below_the_threshold_is_not_published():
+    reg = build_place_registry(_df(_town(MIN_SCHOOLS - 1, "Crosby", "Sefton")))
+    assert "town:crosby" not in reg
+
+
+def test_a_town_below_threshold_still_names_its_authority():
+    # The route layer needs somewhere to 301 to.
+    reg = build_place_registry(_df(
+        _town(MIN_SCHOOLS - 1, "Crosby", "Sefton") + _town(MIN_SCHOOLS, "Bootle", "Sefton")))
+    assert "authority:sefton" in reg
+
+
+def test_town_and_authority_of_the_same_name_are_separate_places():
+    # 67 real collisions. Neither set contains the other: Bedford the town has
+    # 104 schools, Bedford the authority 86, because postal towns cross
+    # authority boundaries.
+    rows = (_town(MIN_SCHOOLS, "Bedford", "Bedford")
+            + _town(MIN_SCHOOLS, "Bedford", "Central Bedfordshire", start=200000))
+    reg = build_place_registry(_df(rows))
+    town, authority = reg["town:bedford"], reg["authority:bedford"]
+    assert set(town.urns) != set(authority.urns)
+    assert len(town.urns) == MIN_SCHOOLS * 2      # both authorities' schools
+    assert len(authority.urns) == MIN_SCHOOLS     # only this authority's
+
+
+def test_schools_without_publishable_data_do_not_count_toward_the_threshold():
+    rows = _town(MIN_SCHOOLS, "Ghosttown", "Nowhere")
+    for r in rows:
+        r["rwm_expected_pct"] = np.nan
+        r["ofsted_grade"] = np.nan
+    reg = build_place_registry(_df(rows))
+    assert "town:ghosttown" not in reg
+
+
+def test_blank_town_is_ignored():
+    rows = _town(MIN_SCHOOLS, "", "Essex")
+    reg = build_place_registry(_df(rows))
+    assert not any(k.startswith("town:") for k in reg)
+
+
+def test_a_school_is_counted_once_even_with_several_years_of_rows():
+    rows = []
+    for year in (202324, 202425):
+        rows += [{**r, "year": year} for r in _town(MIN_SCHOOLS, "Beccles", "Suffolk")]
+    reg = build_place_registry(_df(rows))
+    assert len(reg["town:beccles"].urns) == MIN_SCHOOLS