school_compare/backend/places.py

"""The place registry: what places the site publishes, and what is in each.

One module owns this question. The pages, the sitemap and the internal-link
modules all read from here, so the threshold and the collision rules exist in
exactly one place and are testable without a browser or a database.

Two namespaces, never one. 67 viable town names collide with a local
authority name, and the authority is the larger set in only 43 of them —
postal towns cross authority boundaries, so neither can absorb the other.
Keys are "<kind>:<slug>" so the collision cannot reappear in the dict.
"""

from __future__ import annotations

import logging
import re
from dataclasses import dataclass

logger = logging.getLogger(__name__)

# Five schools with publishable data. Below this a place has nothing to say
# that a list of schools does not, and publishing it is index bloat.
MIN_SCHOOLS = 5


@dataclass(frozen=True)
class Place:
    kind: str                       # "town" | "locality" | "authority" | "outcode"
    slug: str
    name: str
    urns: tuple[int, ...]
    parent_authority: str | None    # authority NAME, for the 301 target

    @property
    def key(self) -> str:
        return f"{self.kind}:{self.slug}"


def _publishable_urns(df) -> set[int]:
    """URNs with something a page could state, deduplicated across years."""
    from backend.app import _PUBLISHABLE_FIELDS

    cols = [c for c in _PUBLISHABLE_FIELDS if c in df.columns]
    if not cols:
        return set()
    return set(df.loc[df[cols].notna().any(axis=1), "urn"].astype(int))


def _parent_authority(group) -> str | None:
    """The most common authority in a group — the useful 301 target.

    A town spanning several authorities has no single parent, so the mode is
    the honest answer rather than an arbitrary first row.
    """
    if "local_authority" not in group.columns:
        return None
    top = group["local_authority"].dropna()
    return str(top.mode().iloc[0]) if not top.empty else None


def _group(df, column: str, kind: str, publishable: set[int]) -> dict[str, Place]:
    """One Place per distinct value of `column` that clears the threshold."""
    from backend.app import _slugify

    if column not in df.columns:
        return {}

    out: dict[str, Place] = {}
    for name, group in df.groupby(column, dropna=True):
        name = str(name).strip()
        if not name:
            continue
        urns = tuple(sorted({int(u) for u in group["urn"]} & publishable))
        if len(urns) < MIN_SCHOOLS:
            continue
        slug = _slugify(name)
        if not slug:
            continue
        place = Place(
            kind=kind, slug=slug, name=name, urns=urns,
            parent_authority=_parent_authority(group) if kind == "town" else None,
        )
        out[place.key] = place
    return out


# "SW11 2AA" -> "SW11". Two letters max, one or two digits, optional letter.
_OUTCODE_RE = re.compile(r"^([A-Z]{1,2}\d{1,2}[A-Z]?)\s")


def _outcode(postcode) -> str | None:
    if not isinstance(postcode, str):
        return None
    m = _OUTCODE_RE.match(postcode.upper().strip())
    return m.group(1) if m else None


def _outcode_places(df, publishable: set[int]) -> dict[str, Place]:
    """One Place per postcode district clearing the threshold.

    These carry no phase variants: nobody searches "primary schools in SW11".
    """
    if "postcode" not in df.columns:
        return {}
    working = df.assign(_oc=df["postcode"].map(_outcode))
    working = working[working["_oc"].notna()]

    out: dict[str, Place] = {}
    for oc, group in working.groupby("_oc"):
        urns = tuple(sorted({int(u) for u in group["urn"]} & publishable))
        if len(urns) < MIN_SCHOOLS:
            continue
        place = Place(kind="outcode", slug=str(oc).lower(), name=str(oc),
                      urns=urns, parent_authority=_parent_authority(group))
        out[place.key] = place
    return out


def _locality_places(df, publishable: set[int],
                     town_slugs: set[str]) -> dict[str, Place]:
    """One Place per curated locality clearing the threshold."""
    from backend.localities import LOCALITY_OUTCODES

    if "postcode" not in df.columns:
        return {}
    working = df.assign(_oc=df["postcode"].map(_outcode))

    out: dict[str, Place] = {}
    for slug, (name, outcodes) in LOCALITY_OUTCODES.items():
        if slug in town_slugs:
            # Skip, do not raise. The guard exists so a locality never
            # silently shadows a town — skipping achieves that, and the error
            # log makes it loud.
            #
            # Raising here took down sitemap generation for all 25,000 school
            # pages when "richmond" met the GIAS town Richmond in North
            # Yorkshire. Worse, GIAS town names change without any code change,
            # so a raise means curated data can break the site spontaneously.
            # A curation mistake must cost one page, not the sitemap.
            logger.error(
                "locality %r collides with the published town of the same "
                "slug and has been skipped; rename it or remove it", slug)
            continue
        group = working[working["_oc"].isin(outcodes)]
        urns = tuple(sorted({int(u) for u in group["urn"]} & publishable))
        if len(urns) < MIN_SCHOOLS:
            # Not an error — a locality can legitimately be too small. Logged
            # because one you meant to publish quietly vanishing is the
            # failure worth hearing about.
            logger.warning(
                "locality %s (%s) has %d publishable schools, below the "
                "threshold of %d - not published",
                slug, ", ".join(outcodes), len(urns), MIN_SCHOOLS)
            continue
        place = Place(kind="locality", slug=slug, name=name, urns=urns,
                      parent_authority=_parent_authority(group))
        out[place.key] = place
    return out


def build_place_registry(df) -> dict[str, Place]:
    """Every place the site publishes, keyed by "<kind>:<slug>"."""
    if df.empty or "urn" not in df.columns:
        return {}

    publishable = _publishable_urns(df)
    registry: dict[str, Place] = {}
    registry.update(_group(df, "local_authority", "authority", publishable))

    towns = _group(df, "town", "town", publishable)
    registry.update(towns)

    town_slugs = {p.slug for p in towns.values()}
    registry.update(_locality_places(df, publishable, town_slugs))
    registry.update(_outcode_places(df, publishable))
    return registry