"""
Implementation of the acutal database accesses for forward search.
"""
+from typing import List, Tuple, AsyncIterator, Dict, Any, Callable, cast
import abc
+import sqlalchemy as sa
+
+from nominatim.typing import SaFromClause, SaScalarSelect, SaColumn, \
+ SaExpression, SaSelect, SaLambdaSelect, SaRow, SaBind
from nominatim.api.connection import SearchConnection
-from nominatim.api.types import SearchDetails
+from nominatim.api.types import SearchDetails, DataLayer, GeometryFormat, Bbox
import nominatim.api.results as nres
from nominatim.api.search.db_search_fields import SearchData, WeightedCategories
+from nominatim.db.sqlalchemy_types import Geometry, IntArray
+
+#pylint: disable=singleton-comparison,not-callable
+#pylint: disable=too-many-branches,too-many-arguments,too-many-locals,too-many-statements
+
+def no_index(expr: SaColumn) -> SaColumn:
+ """ Wrap the given expression, so that the query planner will
+ refrain from using the expression for index lookup.
+ """
+ return sa.func.coalesce(sa.null(), expr) # pylint: disable=not-callable
+
+
+def _details_to_bind_params(details: SearchDetails) -> Dict[str, Any]:
+ """ Create a dictionary from search parameters that can be used
+ as bind parameter for SQL execute.
+ """
+ return {'limit': details.max_results,
+ 'min_rank': details.min_rank,
+ 'max_rank': details.max_rank,
+ 'viewbox': details.viewbox,
+ 'viewbox2': details.viewbox_x2,
+ 'near': details.near,
+ 'near_radius': details.near_radius,
+ 'excluded': details.excluded,
+ 'countries': details.countries}
+
+
+LIMIT_PARAM: SaBind = sa.bindparam('limit')
+MIN_RANK_PARAM: SaBind = sa.bindparam('min_rank')
+MAX_RANK_PARAM: SaBind = sa.bindparam('max_rank')
+VIEWBOX_PARAM: SaBind = sa.bindparam('viewbox', type_=Geometry)
+VIEWBOX2_PARAM: SaBind = sa.bindparam('viewbox2', type_=Geometry)
+NEAR_PARAM: SaBind = sa.bindparam('near', type_=Geometry)
+NEAR_RADIUS_PARAM: SaBind = sa.bindparam('near_radius')
+COUNTRIES_PARAM: SaBind = sa.bindparam('countries')
+
+
+def filter_by_area(sql: SaSelect, t: SaFromClause,
+ details: SearchDetails, avoid_index: bool = False) -> SaSelect:
+ """ Apply SQL statements for filtering by viewbox and near point,
+ if applicable.
+ """
+ if details.near is not None and details.near_radius is not None:
+ if details.near_radius < 0.1 and not avoid_index:
+ sql = sql.where(t.c.geometry.within_distance(NEAR_PARAM, NEAR_RADIUS_PARAM))
+ else:
+ sql = sql.where(t.c.geometry.ST_Distance(NEAR_PARAM) <= NEAR_RADIUS_PARAM)
+ if details.viewbox is not None and details.bounded_viewbox:
+ sql = sql.where(t.c.geometry.intersects(VIEWBOX_PARAM,
+ use_index=not avoid_index and
+ details.viewbox.area < 0.2))
+
+ return sql
+
+
+def _exclude_places(t: SaFromClause) -> Callable[[], SaExpression]:
+ return lambda: t.c.place_id.not_in(sa.bindparam('excluded'))
+
+
+def _select_placex(t: SaFromClause) -> SaSelect:
+ return sa.select(t.c.place_id, t.c.osm_type, t.c.osm_id, t.c.name,
+ t.c.class_, t.c.type,
+ t.c.address, t.c.extratags,
+ t.c.housenumber, t.c.postcode, t.c.country_code,
+ t.c.wikipedia,
+ t.c.parent_place_id, t.c.rank_address, t.c.rank_search,
+ t.c.linked_place_id, t.c.admin_level,
+ t.c.centroid,
+ t.c.geometry.ST_Expand(0).label('bbox'))
+
+
+def _add_geometry_columns(sql: SaLambdaSelect, col: SaColumn, details: SearchDetails) -> SaSelect:
+ out = []
+
+ if details.geometry_simplification > 0.0:
+ col = sa.func.ST_SimplifyPreserveTopology(col, details.geometry_simplification)
+
+ if details.geometry_output & GeometryFormat.GEOJSON:
+ out.append(sa.func.ST_AsGeoJSON(col, 7).label('geometry_geojson'))
+ if details.geometry_output & GeometryFormat.TEXT:
+ out.append(sa.func.ST_AsText(col).label('geometry_text'))
+ if details.geometry_output & GeometryFormat.KML:
+ out.append(sa.func.ST_AsKML(col, 7).label('geometry_kml'))
+ if details.geometry_output & GeometryFormat.SVG:
+ out.append(sa.func.ST_AsSVG(col, 0, 7).label('geometry_svg'))
+
+ return sql.add_columns(*out)
+
+
+def _make_interpolation_subquery(table: SaFromClause, inner: SaFromClause,
+ numerals: List[int], details: SearchDetails) -> SaScalarSelect:
+ all_ids = sa.func.ArrayAgg(table.c.place_id)
+ sql = sa.select(all_ids).where(table.c.parent_place_id == inner.c.place_id)
+
+ if len(numerals) == 1:
+ sql = sql.where(sa.between(numerals[0], table.c.startnumber, table.c.endnumber))\
+ .where((numerals[0] - table.c.startnumber) % table.c.step == 0)
+ else:
+ sql = sql.where(sa.or_(
+ *(sa.and_(sa.between(n, table.c.startnumber, table.c.endnumber),
+ (n - table.c.startnumber) % table.c.step == 0)
+ for n in numerals)))
+
+ if details.excluded:
+ sql = sql.where(_exclude_places(table))
+
+ return sql.scalar_subquery()
+
+
+def _filter_by_layer(table: SaFromClause, layers: DataLayer) -> SaColumn:
+ orexpr: List[SaExpression] = []
+ if layers & DataLayer.ADDRESS and layers & DataLayer.POI:
+ orexpr.append(no_index(table.c.rank_address).between(1, 30))
+ elif layers & DataLayer.ADDRESS:
+ orexpr.append(no_index(table.c.rank_address).between(1, 29))
+ orexpr.append(sa.func.IsAddressPoint(table))
+ elif layers & DataLayer.POI:
+ orexpr.append(sa.and_(no_index(table.c.rank_address) == 30,
+ table.c.class_.not_in(('place', 'building'))))
+
+ if layers & DataLayer.MANMADE:
+ exclude = []
+ if not layers & DataLayer.RAILWAY:
+ exclude.append('railway')
+ if not layers & DataLayer.NATURAL:
+ exclude.extend(('natural', 'water', 'waterway'))
+ orexpr.append(sa.and_(table.c.class_.not_in(tuple(exclude)),
+ no_index(table.c.rank_address) == 0))
+ else:
+ include = []
+ if layers & DataLayer.RAILWAY:
+ include.append('railway')
+ if layers & DataLayer.NATURAL:
+ include.extend(('natural', 'water', 'waterway'))
+ orexpr.append(sa.and_(table.c.class_.in_(tuple(include)),
+ no_index(table.c.rank_address) == 0))
+
+ if len(orexpr) == 1:
+ return orexpr[0]
+
+ return sa.or_(*orexpr)
+
+
+def _interpolated_position(table: SaFromClause, nr: SaColumn) -> SaColumn:
+ pos = sa.cast(nr - table.c.startnumber, sa.Float) / (table.c.endnumber - table.c.startnumber)
+ return sa.case(
+ (table.c.endnumber == table.c.startnumber, table.c.linegeo.ST_Centroid()),
+ else_=table.c.linegeo.ST_LineInterpolatePoint(pos)).label('centroid')
+
+
+async def _get_placex_housenumbers(conn: SearchConnection,
+ place_ids: List[int],
+ details: SearchDetails) -> AsyncIterator[nres.SearchResult]:
+ t = conn.t.placex
+ sql = _select_placex(t).add_columns(t.c.importance)\
+ .where(t.c.place_id.in_(place_ids))
+
+ if details.geometry_output:
+ sql = _add_geometry_columns(sql, t.c.geometry, details)
+
+ for row in await conn.execute(sql):
+ result = nres.create_from_placex_row(row, nres.SearchResult)
+ assert result
+ result.bbox = Bbox.from_wkb(row.bbox)
+ yield result
+
+
+def _int_list_to_subquery(inp: List[int]) -> 'sa.Subquery':
+ """ Create a subselect that returns the given list of integers
+ as rows in the column 'nr'.
+ """
+ vtab = sa.func.JsonArrayEach(sa.type_coerce(inp, sa.JSON))\
+ .table_valued(sa.column('value', type_=sa.JSON))
+ return sa.select(sa.cast(sa.cast(vtab.c.value, sa.Text), sa.Integer).label('nr')).subquery()
+
+
+async def _get_osmline(conn: SearchConnection, place_ids: List[int],
+ numerals: List[int],
+ details: SearchDetails) -> AsyncIterator[nres.SearchResult]:
+ t = conn.t.osmline
+
+ values = _int_list_to_subquery(numerals)
+ sql = sa.select(t.c.place_id, t.c.osm_id,
+ t.c.parent_place_id, t.c.address,
+ values.c.nr.label('housenumber'),
+ _interpolated_position(t, values.c.nr),
+ t.c.postcode, t.c.country_code)\
+ .where(t.c.place_id.in_(place_ids))\
+ .join(values, values.c.nr.between(t.c.startnumber, t.c.endnumber))
+
+ if details.geometry_output:
+ sub = sql.subquery()
+ sql = _add_geometry_columns(sa.select(sub), sub.c.centroid, details)
+
+ for row in await conn.execute(sql):
+ result = nres.create_from_osmline_row(row, nres.SearchResult)
+ assert result
+ yield result
+
+
+async def _get_tiger(conn: SearchConnection, place_ids: List[int],
+ numerals: List[int], osm_id: int,
+ details: SearchDetails) -> AsyncIterator[nres.SearchResult]:
+ t = conn.t.tiger
+ values = _int_list_to_subquery(numerals)
+ sql = sa.select(t.c.place_id, t.c.parent_place_id,
+ sa.literal('W').label('osm_type'),
+ sa.literal(osm_id).label('osm_id'),
+ values.c.nr.label('housenumber'),
+ _interpolated_position(t, values.c.nr),
+ t.c.postcode)\
+ .where(t.c.place_id.in_(place_ids))\
+ .join(values, values.c.nr.between(t.c.startnumber, t.c.endnumber))
+
+ if details.geometry_output:
+ sub = sql.subquery()
+ sql = _add_geometry_columns(sa.select(sub), sub.c.centroid, details)
+
+ for row in await conn.execute(sql):
+ result = nres.create_from_tiger_row(row, nres.SearchResult)
+ assert result
+ yield result
+
class AbstractSearch(abc.ABC):
""" Encapuslation of a single lookup in the database.
"""
+ SEARCH_PRIO: int = 2
def __init__(self, penalty: float) -> None:
self.penalty = penalty
details: SearchDetails) -> nres.SearchResults:
""" Find results for the search in the database.
"""
- return nres.SearchResults([])
+ 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 = _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(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(_exclude_places(t))
+ if details.layers is not None:
+ sql = sql.where(_filter_by_layer(t, details.layers))
+
+ sql = sql.limit(LIMIT_PARAM)
+ for row in await conn.execute(sql, _details_to_bind_params(details)):
+ 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)
+
class PoiSearch(AbstractSearch):
"""
def __init__(self, sdata: SearchData) -> None:
super().__init__(sdata.penalty)
- self.categories = sdata.qualifiers
+ self.qualifiers = sdata.qualifiers
self.countries = sdata.countries
details: SearchDetails) -> nres.SearchResults:
""" Find results for the search in the database.
"""
- return nres.SearchResults([])
+ bind_params = _details_to_bind_params(details)
+ t = conn.t.placex
+
+ rows: List[SaRow] = []
+
+ if details.near and details.near_radius is not None and details.near_radius < 0.2:
+ # simply search in placex table
+ def _base_query() -> SaSelect:
+ return _select_placex(t) \
+ .add_columns((-t.c.centroid.ST_Distance(NEAR_PARAM))
+ .label('importance'))\
+ .where(t.c.linked_place_id == None) \
+ .where(t.c.geometry.within_distance(NEAR_PARAM, NEAR_RADIUS_PARAM)) \
+ .order_by(t.c.centroid.ST_Distance(NEAR_PARAM)) \
+ .limit(LIMIT_PARAM)
+
+ classtype = self.qualifiers.values
+ if len(classtype) == 1:
+ cclass, ctype = classtype[0]
+ sql: SaLambdaSelect = sa.lambda_stmt(lambda: _base_query()
+ .where(t.c.class_ == cclass)
+ .where(t.c.type == ctype))
+ else:
+ sql = _base_query().where(sa.or_(*(sa.and_(t.c.class_ == cls, t.c.type == typ)
+ for cls, typ in classtype)))
+
+ if self.countries:
+ sql = sql.where(t.c.country_code.in_(self.countries.values))
+
+ if details.viewbox is not None and details.bounded_viewbox:
+ sql = sql.where(t.c.geometry.intersects(VIEWBOX_PARAM))
+
+ rows.extend(await conn.execute(sql, bind_params))
+ else:
+ # use the class type tables
+ for category in self.qualifiers.values:
+ table = await conn.get_class_table(*category)
+ if table is not None:
+ sql = _select_placex(t)\
+ .add_columns(t.c.importance)\
+ .join(table, t.c.place_id == table.c.place_id)\
+ .where(t.c.class_ == category[0])\
+ .where(t.c.type == category[1])
+
+ if details.viewbox is not None and details.bounded_viewbox:
+ sql = sql.where(table.c.centroid.intersects(VIEWBOX_PARAM))
+
+ if details.near and details.near_radius is not None:
+ sql = sql.order_by(table.c.centroid.ST_Distance(NEAR_PARAM))\
+ .where(table.c.centroid.within_distance(NEAR_PARAM,
+ NEAR_RADIUS_PARAM))
+
+ if self.countries:
+ sql = sql.where(t.c.country_code.in_(self.countries.values))
+
+ sql = sql.limit(LIMIT_PARAM)
+ rows.extend(await conn.execute(sql, bind_params))
+
+ results = nres.SearchResults()
+ for row in rows:
+ result = nres.create_from_placex_row(row, nres.SearchResult)
+ assert result
+ result.accuracy = self.penalty + self.qualifiers.get_penalty((row.class_, row.type))
+ result.bbox = Bbox.from_wkb(row.bbox)
+ results.append(result)
+
+ return results
class CountrySearch(AbstractSearch):
""" Search for a country name or country code.
"""
+ SEARCH_PRIO = 0
+
def __init__(self, sdata: SearchData) -> None:
super().__init__(sdata.penalty)
self.countries = sdata.countries
details: SearchDetails) -> nres.SearchResults:
""" Find results for the search in the database.
"""
- return nres.SearchResults([])
+ t = conn.t.placex
+
+ ccodes = self.countries.values
+ sql = _select_placex(t)\
+ .add_columns(t.c.importance)\
+ .where(t.c.country_code.in_(ccodes))\
+ .where(t.c.rank_address == 4)
+
+ if details.geometry_output:
+ sql = _add_geometry_columns(sql, t.c.geometry, details)
+
+ if details.excluded:
+ sql = sql.where(_exclude_places(t))
+
+ sql = filter_by_area(sql, t, details)
+
+ results = nres.SearchResults()
+ for row in await conn.execute(sql, _details_to_bind_params(details)):
+ result = nres.create_from_placex_row(row, nres.SearchResult)
+ assert result
+ result.accuracy = self.penalty + self.countries.get_penalty(row.country_code, 5.0)
+ result.bbox = Bbox.from_wkb(row.bbox)
+ results.append(result)
+
+ if not results:
+ results = await self.lookup_in_country_table(conn, details)
+
+ if results:
+ details.min_rank = min(5, details.max_rank)
+ details.max_rank = min(25, details.max_rank)
+
+ return results
+
+
+ async def lookup_in_country_table(self, conn: SearchConnection,
+ details: SearchDetails) -> nres.SearchResults:
+ """ Look up the country in the fallback country tables.
+ """
+ # Avoid the fallback search when this is a more search. Country results
+ # usually are in the first batch of results and it is not possible
+ # to exclude these fallbacks.
+ if details.excluded:
+ return nres.SearchResults()
+
+ t = conn.t.country_name
+ tgrid = conn.t.country_grid
+
+ sql = sa.select(tgrid.c.country_code,
+ tgrid.c.geometry.ST_Centroid().ST_Collect().ST_Centroid()
+ .label('centroid'),
+ tgrid.c.geometry.ST_Collect().ST_Expand(0).label('bbox'))\
+ .where(tgrid.c.country_code.in_(self.countries.values))\
+ .group_by(tgrid.c.country_code)
+
+ sql = filter_by_area(sql, tgrid, details, avoid_index=True)
+
+ sub = sql.subquery('grid')
+
+ sql = sa.select(t.c.country_code,
+ t.c.name.merge(t.c.derived_name).label('name'),
+ sub.c.centroid, sub.c.bbox)\
+ .join(sub, t.c.country_code == sub.c.country_code)
+
+ if details.geometry_output:
+ sql = _add_geometry_columns(sql, sub.c.centroid, details)
+
+ results = nres.SearchResults()
+ for row in await conn.execute(sql, _details_to_bind_params(details)):
+ result = nres.create_from_country_row(row, nres.SearchResult)
+ assert result
+ result.bbox = Bbox.from_wkb(row.bbox)
+ result.accuracy = self.penalty + self.countries.get_penalty(row.country_code, 5.0)
+ results.append(result)
+
+ return results
+
class PostcodeSearch(AbstractSearch):
details: SearchDetails) -> nres.SearchResults:
""" Find results for the search in the database.
"""
- return nres.SearchResults([])
+ t = conn.t.postcode
+ pcs = self.postcodes.values
+
+ sql = sa.select(t.c.place_id, t.c.parent_place_id,
+ t.c.rank_search, t.c.rank_address,
+ t.c.postcode, t.c.country_code,
+ t.c.geometry.label('centroid'))\
+ .where(t.c.postcode.in_(pcs))
+
+ if details.geometry_output:
+ sql = _add_geometry_columns(sql, t.c.geometry, details)
+
+ penalty: SaExpression = sa.literal(self.penalty)
+
+ if details.viewbox is not None and not details.bounded_viewbox:
+ penalty += sa.case((t.c.geometry.intersects(VIEWBOX_PARAM), 0.0),
+ (t.c.geometry.intersects(VIEWBOX2_PARAM), 0.5),
+ else_=1.0)
+
+ if details.near is not None:
+ sql = sql.order_by(t.c.geometry.ST_Distance(NEAR_PARAM))
+
+ sql = filter_by_area(sql, t, details)
+
+ if self.countries:
+ sql = sql.where(t.c.country_code.in_(self.countries.values))
+
+ if details.excluded:
+ sql = sql.where(_exclude_places(t))
+
+ if self.lookups:
+ assert len(self.lookups) == 1
+ tsearch = conn.t.search_name
+ sql = sql.where(tsearch.c.place_id == t.c.parent_place_id)\
+ .where((tsearch.c.name_vector + tsearch.c.nameaddress_vector)
+ .contains(sa.type_coerce(self.lookups[0].tokens,
+ IntArray)))
+
+ for ranking in self.rankings:
+ penalty += ranking.sql_penalty(conn.t.search_name)
+ penalty += sa.case(*((t.c.postcode == v, p) for v, p in self.postcodes),
+ else_=1.0)
+
+
+ sql = sql.add_columns(penalty.label('accuracy'))
+ sql = sql.order_by('accuracy').limit(LIMIT_PARAM)
+
+ results = nres.SearchResults()
+ for row in await conn.execute(sql, _details_to_bind_params(details)):
+ result = nres.create_from_postcode_row(row, nres.SearchResult)
+ assert result
+ result.accuracy = row.accuracy
+ results.append(result)
+
+ return results
+
class PlaceSearch(AbstractSearch):
""" Generic search for an address or named place.
"""
+ SEARCH_PRIO = 1
+
def __init__(self, extra_penalty: float, sdata: SearchData, expected_count: int) -> None:
super().__init__(sdata.penalty + extra_penalty)
self.countries = sdata.countries
details: SearchDetails) -> nres.SearchResults:
""" Find results for the search in the database.
"""
- return nres.SearchResults([])
+ t = conn.t.placex
+ tsearch = conn.t.search_name
+
+ sql: SaLambdaSelect = sa.lambda_stmt(lambda:
+ _select_placex(t).where(t.c.place_id == tsearch.c.place_id))
+
+
+ if details.geometry_output:
+ sql = _add_geometry_columns(sql, t.c.geometry, details)
+
+ penalty: SaExpression = sa.literal(self.penalty)
+ for ranking in self.rankings:
+ penalty += ranking.sql_penalty(tsearch)
+
+ for lookup in self.lookups:
+ sql = sql.where(lookup.sql_condition(tsearch))
+
+ if self.countries:
+ sql = sql.where(tsearch.c.country_code.in_(self.countries.values))
+
+ if self.postcodes:
+ # if a postcode is given, don't search for state or country level objects
+ sql = sql.where(tsearch.c.address_rank > 9)
+ tpc = conn.t.postcode
+ pcs = self.postcodes.values
+ if self.expected_count > 5000:
+ # Many results expected. Restrict by postcode.
+ sql = sql.where(sa.select(tpc.c.postcode)
+ .where(tpc.c.postcode.in_(pcs))
+ .where(tsearch.c.centroid.within_distance(tpc.c.geometry, 0.12))
+ .exists())
+
+ # Less results, only have a preference for close postcodes
+ pc_near = sa.select(sa.func.min(tpc.c.geometry.ST_Distance(tsearch.c.centroid)))\
+ .where(tpc.c.postcode.in_(pcs))\
+ .scalar_subquery()
+ penalty += sa.case((t.c.postcode.in_(pcs), 0.0),
+ else_=sa.func.coalesce(pc_near, cast(SaColumn, 2.0)))
+
+ if details.viewbox is not None:
+ if details.bounded_viewbox:
+ sql = sql.where(tsearch.c.centroid
+ .intersects(VIEWBOX_PARAM,
+ use_index=details.viewbox.area < 0.2))
+ elif not self.postcodes and not self.housenumbers and self.expected_count >= 10000:
+ sql = sql.where(tsearch.c.centroid
+ .intersects(VIEWBOX2_PARAM,
+ use_index=details.viewbox.area < 0.5))
+ else:
+ penalty += sa.case((t.c.geometry.intersects(VIEWBOX_PARAM, use_index=False), 0.0),
+ (t.c.geometry.intersects(VIEWBOX2_PARAM, use_index=False), 0.5),
+ else_=1.0)
+
+ if details.near is not None:
+ if details.near_radius is not None:
+ if details.near_radius < 0.1:
+ sql = sql.where(tsearch.c.centroid.within_distance(NEAR_PARAM,
+ NEAR_RADIUS_PARAM))
+ else:
+ sql = sql.where(tsearch.c.centroid
+ .ST_Distance(NEAR_PARAM) < NEAR_RADIUS_PARAM)
+ sql = sql.add_columns((-tsearch.c.centroid.ST_Distance(NEAR_PARAM))
+ .label('importance'))
+ sql = sql.order_by(sa.desc(sa.text('importance')))
+ else:
+ if self.expected_count < 10000\
+ or (details.viewbox is not None and details.viewbox.area < 0.5):
+ sql = sql.order_by(
+ penalty - sa.case((tsearch.c.importance > 0, tsearch.c.importance),
+ else_=0.75001-(sa.cast(tsearch.c.search_rank, sa.Float())/40)))
+ sql = sql.add_columns(t.c.importance)
+
+
+ sql = sql.add_columns(penalty.label('accuracy'))
+
+ if self.expected_count < 10000:
+ sql = sql.order_by(sa.text('accuracy'))
+
+ if self.housenumbers:
+ hnr_list = '|'.join(self.housenumbers.values)
+ sql = sql.where(tsearch.c.address_rank.between(16, 30))\
+ .where(sa.or_(tsearch.c.address_rank < 30,
+ sa.func.RegexpWord(hnr_list, t.c.housenumber)))
+
+ # Cross check for housenumbers, need to do that on a rather large
+ # set. Worst case there are 40.000 main streets in OSM.
+ inner = sql.limit(10000).subquery()
+
+ # Housenumbers from placex
+ thnr = conn.t.placex.alias('hnr')
+ pid_list = sa.func.ArrayAgg(thnr.c.place_id)
+ place_sql = sa.select(pid_list)\
+ .where(thnr.c.parent_place_id == inner.c.place_id)\
+ .where(sa.func.RegexpWord(hnr_list, thnr.c.housenumber))\
+ .where(thnr.c.linked_place_id == None)\
+ .where(thnr.c.indexed_status == 0)
+
+ if details.excluded:
+ place_sql = place_sql.where(thnr.c.place_id.not_in(sa.bindparam('excluded')))
+ if self.qualifiers:
+ place_sql = place_sql.where(self.qualifiers.sql_restrict(thnr))
+
+ numerals = [int(n) for n in self.housenumbers.values
+ if n.isdigit() and len(n) < 8]
+ interpol_sql: SaColumn
+ tiger_sql: SaColumn
+ if numerals and \
+ (not self.qualifiers or ('place', 'house') in self.qualifiers.values):
+ # Housenumbers from interpolations
+ interpol_sql = _make_interpolation_subquery(conn.t.osmline, inner,
+ numerals, details)
+ # Housenumbers from Tiger
+ tiger_sql = sa.case((inner.c.country_code == 'us',
+ _make_interpolation_subquery(conn.t.tiger, inner,
+ numerals, details)
+ ), else_=None)
+ else:
+ interpol_sql = sa.null()
+ tiger_sql = sa.null()
+
+ unsort = sa.select(inner, place_sql.scalar_subquery().label('placex_hnr'),
+ interpol_sql.label('interpol_hnr'),
+ tiger_sql.label('tiger_hnr')).subquery('unsort')
+ sql = sa.select(unsort)\
+ .order_by(sa.case((unsort.c.placex_hnr != None, 1),
+ (unsort.c.interpol_hnr != None, 2),
+ (unsort.c.tiger_hnr != None, 3),
+ else_=4),
+ unsort.c.accuracy)
+ else:
+ sql = sql.where(t.c.linked_place_id == None)\
+ .where(t.c.indexed_status == 0)
+ if self.qualifiers:
+ sql = sql.where(self.qualifiers.sql_restrict(t))
+ if details.excluded:
+ sql = sql.where(_exclude_places(tsearch))
+ if details.min_rank > 0:
+ sql = sql.where(sa.or_(tsearch.c.address_rank >= MIN_RANK_PARAM,
+ tsearch.c.search_rank >= MIN_RANK_PARAM))
+ if details.max_rank < 30:
+ sql = sql.where(sa.or_(tsearch.c.address_rank <= MAX_RANK_PARAM,
+ tsearch.c.search_rank <= MAX_RANK_PARAM))
+ if details.layers is not None:
+ sql = sql.where(_filter_by_layer(t, details.layers))
+
+ sql = sql.limit(LIMIT_PARAM)
+
+ results = nres.SearchResults()
+ for row in await conn.execute(sql, _details_to_bind_params(details)):
+ result = nres.create_from_placex_row(row, nres.SearchResult)
+ assert result
+ result.bbox = Bbox.from_wkb(row.bbox)
+ result.accuracy = row.accuracy
+ if self.housenumbers and row.rank_address < 30:
+ if row.placex_hnr:
+ subs = _get_placex_housenumbers(conn, row.placex_hnr, details)
+ elif row.interpol_hnr:
+ subs = _get_osmline(conn, row.interpol_hnr, numerals, details)
+ elif row.tiger_hnr:
+ subs = _get_tiger(conn, row.tiger_hnr, numerals, row.osm_id, details)
+ else:
+ subs = None
+
+ if subs is not None:
+ async for sub in subs:
+ assert sub.housenumber
+ sub.accuracy = result.accuracy
+ if not any(nr in self.housenumbers.values
+ for nr in sub.housenumber.split(';')):
+ sub.accuracy += 0.6
+ results.append(sub)
+
+ # Only add the street as a result, if it meets all other
+ # filter conditions.
+ if (not details.excluded or result.place_id not in details.excluded)\
+ and (not self.qualifiers or result.category in self.qualifiers.values)\
+ and result.rank_address >= details.min_rank:
+ result.accuracy += 1.0 # penalty for missing housenumber
+ results.append(result)
+ else:
+ results.append(result)
+
+ return results
projects / nominatim.git / blobdiff