split db_searches moving each class in its own file

src/nominatim_api/search/db_searches/near_search.py

@@ -0,0 +1,136 @@
+# SPDX-License-Identifier: GPL-3.0-or-later
+#
+# This file is part of Nominatim. (https://nominatim.org)
+#
+# Copyright (C) 2025 by the Nominatim developer community.
+# For a full list of authors see the git log.
+"""
+Implementation of a category search around a place.
+"""
+from typing import List, Tuple
+
+import sqlalchemy as sa
+
+from . import base
+from ...typing import SaBind
+from ...types import SearchDetails, Bbox
+from ...connection import SearchConnection
+from ... import results as nres
+from ..db_search_fields import WeightedCategories
+
+
+LIMIT_PARAM: SaBind = sa.bindparam('limit')
+MIN_RANK_PARAM: SaBind = sa.bindparam('min_rank')
+MAX_RANK_PARAM: SaBind = sa.bindparam('max_rank')
+COUNTRIES_PARAM: SaBind = sa.bindparam('countries')
+
+
+class NearSearch(base.AbstractSearch):
+    """ Category search of a place type near the result of another search.
+    """
+    def __init__(self, penalty: float, categories: WeightedCategories,
+                 search: base.AbstractSearch) -> None:
+        super().__init__(penalty)
+        self.search = search
+        self.categories = categories
+
+    async def lookup(self, conn: SearchConnection,
+                     details: SearchDetails) -> nres.SearchResults:
+        """ Find results for the search in the database.
+        """
+        results = nres.SearchResults()
+        base = await self.search.lookup(conn, details)
+
+        if not base:
+            return results
+
+        base.sort(key=lambda r: (r.accuracy, r.rank_search))
+        max_accuracy = base[0].accuracy + 0.5
+        if base[0].rank_address == 0:
+            min_rank = 0
+            max_rank = 0
+        elif base[0].rank_address < 26:
+            min_rank = 1
+            max_rank = min(25, base[0].rank_address + 4)
+        else:
+            min_rank = 26
+            max_rank = 30
+        base = nres.SearchResults(r for r in base
+                                  if (r.source_table == nres.SourceTable.PLACEX
+                                      and r.accuracy <= max_accuracy
+                                      and r.bbox and r.bbox.area < 20
+                                      and r.rank_address >= min_rank
+                                      and r.rank_address <= max_rank))
+
+        if base:
+            baseids = [b.place_id for b in base[:5] if b.place_id]
+
+            for category, penalty in self.categories:
+                await self.lookup_category(results, conn, baseids, category, penalty, details)
+                if len(results) >= details.max_results:
+                    break
+
+        return results
+
+    async def lookup_category(self, results: nres.SearchResults,
+                              conn: SearchConnection, ids: List[int],
+                              category: Tuple[str, str], penalty: float,
+                              details: SearchDetails) -> None:
+        """ Find places of the given category near the list of
+            place ids and add the results to 'results'.
+        """
+        table = await conn.get_class_table(*category)
+
+        tgeom = conn.t.placex.alias('pgeom')
+
+        if table is None:
+            # No classtype table available, do a simplified lookup in placex.
+            table = conn.t.placex
+            sql = sa.select(table.c.place_id,
+                            sa.func.min(tgeom.c.centroid.ST_Distance(table.c.centroid))
+                              .label('dist'))\
+                    .join(tgeom, table.c.geometry.intersects(tgeom.c.centroid.ST_Expand(0.01)))\
+                    .where(table.c.class_ == category[0])\
+                    .where(table.c.type == category[1])
+        else:
+            # Use classtype table. We can afford to use a larger
+            # radius for the lookup.
+            sql = sa.select(table.c.place_id,
+                            sa.func.min(tgeom.c.centroid.ST_Distance(table.c.centroid))
+                              .label('dist'))\
+                    .join(tgeom,
+                          table.c.centroid.ST_CoveredBy(
+                              sa.case((sa.and_(tgeom.c.rank_address > 9,
+                                               tgeom.c.geometry.is_area()),
+                                       tgeom.c.geometry),
+                                      else_=tgeom.c.centroid.ST_Expand(0.05))))
+
+        inner = sql.where(tgeom.c.place_id.in_(ids))\
+                   .group_by(table.c.place_id).subquery()
+
+        t = conn.t.placex
+        sql = base.select_placex(t).add_columns((-inner.c.dist).label('importance'))\
+                                   .join(inner, inner.c.place_id == t.c.place_id)\
+                                   .order_by(inner.c.dist)
+
+        sql = sql.where(base.no_index(t.c.rank_address).between(MIN_RANK_PARAM, MAX_RANK_PARAM))
+        if details.countries:
+            sql = sql.where(t.c.country_code.in_(COUNTRIES_PARAM))
+        if details.excluded:
+            sql = sql.where(base.exclude_places(t))
+        if details.layers is not None:
+            sql = sql.where(base.filter_by_layer(t, details.layers))
+
+        sql = sql.limit(LIMIT_PARAM)
+
+        bind_params = {'limit': details.max_results,
+                       'min_rank': details.min_rank,
+                       'max_rank': details.max_rank,
+                       'excluded': details.excluded,
+                       'countries': details.countries}
+        for row in await conn.execute(sql, bind_params):
+            result = nres.create_from_placex_row(row, nres.SearchResult)
+            assert result
+            result.accuracy = self.penalty + penalty
+            result.bbox = Bbox.from_wkb(row.bbox)
+            results.append(result)