"""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


def test_locality_groups_schools_by_outcode(monkeypatch):
    # The GIAS town field collapses 1,819 London schools into "London", so a
    # locality is defined by its postcode districts instead.
    from backend import localities
    monkeypatch.setattr(localities, "LOCALITY_OUTCODES",
                        {"battersea": ("Battersea", ("SW11",))})
    rows = _town(MIN_SCHOOLS, "London", "Wandsworth")
    for r in rows:
        r["postcode"] = "SW11 2AA"
    reg = build_place_registry(_df(rows))
    assert reg["locality:battersea"].name == "Battersea"
    assert len(reg["locality:battersea"].urns) == MIN_SCHOOLS


def test_locality_below_the_threshold_is_not_published(monkeypatch):
    from backend import localities
    monkeypatch.setattr(localities, "LOCALITY_OUTCODES",
                        {"nowhere": ("Nowhere", ("ZZ99",))})
    reg = build_place_registry(_df(_town(MIN_SCHOOLS, "London", "Wandsworth")))
    assert "locality:nowhere" not in reg


def test_a_locality_may_not_shadow_a_viable_town(monkeypatch):
    # Silently shadowing a town would lose a page carrying real demand.
    from backend import localities
    monkeypatch.setattr(localities, "LOCALITY_OUTCODES",
                        {"brentwood": ("Brentwood", ("CM13",))})
    rows = _town(MIN_SCHOOLS, "Brentwood", "Essex")
    for r in rows:
        r["postcode"] = "CM13 1AA"
    with pytest.raises(ValueError, match="brentwood"):
        build_place_registry(_df(rows))


def test_outcode_places_are_built_from_postcodes():
    rows = _town(MIN_SCHOOLS, "Brentwood", "Essex")
    for r in rows:
        r["postcode"] = "CM13 1AA"
    reg = build_place_registry(_df(rows))
    assert reg["outcode:cm13"].name == "CM13"
    assert len(reg["outcode:cm13"].urns) == MIN_SCHOOLS


def test_malformed_postcodes_do_not_create_places():
    rows = _town(MIN_SCHOOLS, "Brentwood", "Essex")
    for r in rows:
        r["postcode"] = "not a postcode"
    reg = build_place_registry(_df(rows))
    assert not any(k.startswith("outcode:") for k in reg)


def test_every_curated_locality_is_structurally_valid():
    # Guards the hand-maintained file: real slug, real name, real outcodes.
    import re
    from backend.localities import LOCALITY_OUTCODES

    assert LOCALITY_OUTCODES, "the curated locality list must not be empty"
    for slug, (name, outcodes) in LOCALITY_OUTCODES.items():
        assert re.fullmatch(r"[a-z0-9-]+", slug), slug
        assert name.strip() == name and name, slug
        assert outcodes, f"{slug} has no outcodes"
        for oc in outcodes:
            assert re.fullmatch(r"[A-Z]{1,2}\d{1,2}[A-Z]?", oc), (slug, oc)


def test_the_pipeline_seed_mirrors_the_canonical_module():
    """Two copies with no drift guard is worse than one copy.

    backend/localities.py is canonical because the backend image does not
    contain pipeline/. The seed exists so the warehouse can join on the same
    definitions, and this is what stops the two diverging — the same
    arrangement assert_gias_code_names_match_seed.sql gives gias_codes.
    """
    import csv
    from pathlib import Path

    from backend.localities import LOCALITY_OUTCODES

    seed_path = (Path(__file__).resolve().parents[2]
                 / "pipeline/transform/seeds/locality_outcodes.csv")
    assert seed_path.exists(), f"missing seed mirror at {seed_path}"

    seed = {
        row["locality_slug"]: (row["locality_name"],
                               tuple(row["outcodes"].split("|")))
        for row in csv.DictReader(seed_path.open())
    }
    assert seed == LOCALITY_OUTCODES