import numpy as np import shapely from shapely.geometry.base import BaseGeometry from . import _compat as compat from . import array, geoseries PREDICATES = {p.name for p in shapely.strtree.BinaryPredicate} | {None} if compat.GEOS_GE_310: PREDICATES.update(["dwithin"]) class SpatialIndex: """A simple wrapper around Shapely's STRTree. Parameters ---------- geometry : np.array of Shapely geometries Geometries from which to build the spatial index. """ def __init__(self, geometry): # set empty geometries to None to avoid segfault on GEOS <= 3.6 # see: # https://github.com/pygeos/pygeos/issues/146 # https://github.com/pygeos/pygeos/issues/147 non_empty = geometry.copy() non_empty[shapely.is_empty(non_empty)] = None # set empty geometries to None to maintain indexing self._tree = shapely.STRtree(non_empty) # store geometries, including empty geometries for user access self.geometries = geometry.copy() @property def valid_query_predicates(self): """Returns valid predicates for the spatial index. Returns ------- set Set of valid predicates for this spatial index. Examples -------- >>> from shapely.geometry import Point >>> s = geopandas.GeoSeries([Point(0, 0), Point(1, 1)]) >>> s.sindex.valid_query_predicates # doctest: +SKIP {None, "contains", "contains_properly", "covered_by", "covers", \ "crosses", "dwithin", "intersects", "overlaps", "touches", "within"} """ return PREDICATES def query( self, geometry, predicate=None, sort=False, distance=None, output_format="tuple" ): """ Return the integer indices of all combinations of each input geometry and tree geometries where the bounding box of each input geometry intersects the bounding box of a tree geometry. If the input geometry is a scalar, this returns an array of shape (n, ) with the indices of the matching tree geometries. If the input geometry is an array_like, this returns an array with shape (2,n) where the subarrays correspond to the indices of the input geometries and indices of the tree geometries associated with each. To generate an array of pairs of input geometry index and tree geometry index, simply transpose the result. If a predicate is provided, the tree geometries are first queried based on the bounding box of the input geometry and then are further filtered to those that meet the predicate when comparing the input geometry to the tree geometry: ``predicate(geometry, tree_geometry)``. The 'dwithin' predicate requires GEOS >= 3.10. Bounding boxes are limited to two dimensions and are axis-aligned (equivalent to the ``bounds`` property of a geometry); any Z values present in input geometries are ignored when querying the tree. Any input geometry that is None or empty will never match geometries in the tree. Parameters ---------- geometry : shapely.Geometry or array-like of geometries \ (numpy.ndarray, GeoSeries, GeometryArray) A single shapely geometry or array of geometries to query against the spatial index. For array-like, accepts both GeoPandas geometry iterables (GeoSeries, GeometryArray) or a numpy array of Shapely geometries. predicate : {None, "contains", "contains_properly", "covered_by", "covers", \ "crosses", "intersects", "overlaps", "touches", "within", "dwithin"}, optional If predicate is provided, the input geometries are tested using the predicate function against each item in the tree whose extent intersects the envelope of the input geometry: ``predicate(input_geometry, tree_geometry)``. If possible, prepared geometries are used to help speed up the predicate operation. sort : bool, default False If True, the results will be sorted in ascending order. In case of 2D array, the result is sorted lexicographically using the geometries' indexes as the primary key and the sindex's indexes as the secondary key. If False, no additional sorting is applied (results are often sorted but there is no guarantee). distance : number or array_like, optional Distances around each input geometry within which to query the tree for the 'dwithin' predicate. If array_like, shape must be broadcastable to shape of geometry. Required if ``predicate='dwithin'``. Returns ------- ndarray with shape (n,) if geometry is a scalar Integer indices for matching geometries from the spatial index tree geometries. OR ndarray with shape (2, n) if geometry is an array_like The first subarray contains input geometry integer indices. The second subarray contains tree geometry integer indices. Examples -------- >>> from shapely.geometry import Point, box >>> s = geopandas.GeoSeries(geopandas.points_from_xy(range(10), range(10))) >>> s 0 POINT (0 0) 1 POINT (1 1) 2 POINT (2 2) 3 POINT (3 3) 4 POINT (4 4) 5 POINT (5 5) 6 POINT (6 6) 7 POINT (7 7) 8 POINT (8 8) 9 POINT (9 9) dtype: geometry Querying the tree with a scalar geometry: >>> s.sindex.query(box(1, 1, 3, 3)) array([1, 2, 3]) >>> s.sindex.query(box(1, 1, 3, 3), predicate="contains") array([2]) Querying the tree with an array of geometries: >>> s2 = geopandas.GeoSeries([box(2, 2, 4, 4), box(5, 5, 6, 6)]) >>> s2 0 POLYGON ((4 2, 4 4, 2 4, 2 2, 4 2)) 1 POLYGON ((6 5, 6 6, 5 6, 5 5, 6 5)) dtype: geometry >>> s.sindex.query(s2) array([[0, 0, 0, 1, 1], [2, 3, 4, 5, 6]]) >>> s.sindex.query(s2, predicate="contains") array([[0], [3]]) >>> s.sindex.query(box(1, 1, 3, 3), predicate="dwithin", distance=0) array([1, 2, 3]) >>> s.sindex.query(box(1, 1, 3, 3), predicate="dwithin", distance=2) array([0, 1, 2, 3, 4]) Notes ----- In the context of a spatial join, input geometries are the "left" geometries that determine the order of the results, and tree geometries are "right" geometries that are joined against the left geometries. This effectively performs an inner join, where only those combinations of geometries that can be joined based on overlapping bounding boxes or optional predicate are returned. """ if predicate not in self.valid_query_predicates: if predicate == "dwithin": raise ValueError("predicate = 'dwithin' requires GEOS >= 3.10.0") raise ValueError( "Got predicate='{}'; ".format(predicate) + "`predicate` must be one of {}".format(self.valid_query_predicates) ) # distance argument requirement of predicate `dwithin` # and only valid for predicate `dwithin` kwargs = {} if predicate == "dwithin": if distance is None: # the distance parameter is needed raise ValueError( "'distance' parameter is required for 'dwithin' predicate" ) # add distance to kwargs kwargs["distance"] = distance elif distance is not None: # distance parameter is invalid raise ValueError( "'distance' parameter is only supported in combination with " "'dwithin' predicate" ) geometry = self._as_geometry_array(geometry) indices = self._tree.query(geometry, predicate=predicate, **kwargs) if output_format != "tuple": sort = True if sort: if indices.ndim == 1: indices = np.sort(indices) else: # sort by first array (geometry) and then second (tree) geo_idx, tree_idx = indices sort_indexer = np.lexsort((tree_idx, geo_idx)) indices = np.vstack((geo_idx[sort_indexer], tree_idx[sort_indexer])) if output_format == "sparse": from scipy.sparse import coo_array return coo_array( (np.ones(len(indices[0]), dtype=np.bool_), indices), shape=(len(self.geometries), len(geometry)), dtype=np.bool_, ) if output_format == "dense": dense = np.zeros((len(self.geometries), len(geometry)), dtype=bool) dense[indices] = True return dense if output_format == "tuple": return indices raise ValueError("Invalid output_format: {}".format(output_format)) @staticmethod def _as_geometry_array(geometry): """Convert geometry into a numpy array of Shapely geometries. Parameters ---------- geometry An array-like of Shapely geometries, a GeoPandas GeoSeries/GeometryArray, shapely.geometry or list of shapely geometries. Returns ------- np.ndarray A numpy array of Shapely geometries. """ if isinstance(geometry, np.ndarray): return array.from_shapely(geometry)._data elif isinstance(geometry, geoseries.GeoSeries): return geometry.values._data elif isinstance(geometry, array.GeometryArray): return geometry._data elif isinstance(geometry, BaseGeometry): return geometry elif geometry is None: return None else: return np.asarray(geometry) def nearest( self, geometry, return_all=True, max_distance=None, return_distance=False, exclusive=False, ): """ Return the nearest geometry in the tree for each input geometry in ``geometry``. If multiple tree geometries have the same distance from an input geometry, multiple results will be returned for that input geometry by default. Specify ``return_all=False`` to only get a single nearest geometry (non-deterministic which nearest is returned). In the context of a spatial join, input geometries are the "left" geometries that determine the order of the results, and tree geometries are "right" geometries that are joined against the left geometries. If ``max_distance`` is not set, this will effectively be a left join because every geometry in ``geometry`` will have a nearest geometry in the tree. However, if ``max_distance`` is used, this becomes an inner join, since some geometries in ``geometry`` may not have a match in the tree. For performance reasons, it is highly recommended that you set the ``max_distance`` parameter. Parameters ---------- geometry : {shapely.geometry, GeoSeries, GeometryArray, numpy.array of Shapely \ geometries} A single shapely geometry, one of the GeoPandas geometry iterables (GeoSeries, GeometryArray), or a numpy array of Shapely geometries to query against the spatial index. return_all : bool, default True If there are multiple equidistant or intersecting nearest geometries, return all those geometries instead of a single nearest geometry. max_distance : float, optional Maximum distance within which to query for nearest items in tree. Must be greater than 0. By default None, indicating no distance limit. return_distance : bool, optional If True, will return distances in addition to indexes. By default False exclusive : bool, optional if True, the nearest geometries that are equal to the input geometry will not be returned. By default False. Requires Shapely >= 2.0. Returns ------- Indices or tuple of (indices, distances) Indices is an ndarray of shape (2,n) and distances (if present) an ndarray of shape (n). The first subarray of indices contains input geometry indices. The second subarray of indices contains tree geometry indices. Examples -------- >>> from shapely.geometry import Point, box >>> s = geopandas.GeoSeries(geopandas.points_from_xy(range(10), range(10))) >>> s.head() 0 POINT (0 0) 1 POINT (1 1) 2 POINT (2 2) 3 POINT (3 3) 4 POINT (4 4) dtype: geometry >>> s.sindex.nearest(Point(1, 1)) array([[0], [1]]) >>> s.sindex.nearest([box(4.9, 4.9, 5.1, 5.1)]) array([[0], [5]]) >>> s2 = geopandas.GeoSeries(geopandas.points_from_xy([7.6, 10], [7.6, 10])) >>> s2 0 POINT (7.6 7.6) 1 POINT (10 10) dtype: geometry >>> s.sindex.nearest(s2) array([[0, 1], [8, 9]]) """ geometry = self._as_geometry_array(geometry) if isinstance(geometry, BaseGeometry) or geometry is None: geometry = [geometry] result = self._tree.query_nearest( geometry, max_distance=max_distance, return_distance=return_distance, all_matches=return_all, exclusive=exclusive, ) if return_distance: indices, distances = result else: indices = result if return_distance: return indices, distances else: return indices def intersection(self, coordinates): """Compatibility wrapper for rtree.index.Index.intersection, use ``query`` instead. Parameters ---------- coordinates : sequence or array Sequence of the form (min_x, min_y, max_x, max_y) to query a rectangle or (x, y) to query a point. Examples -------- >>> from shapely.geometry import Point, box >>> s = geopandas.GeoSeries(geopandas.points_from_xy(range(10), range(10))) >>> s 0 POINT (0 0) 1 POINT (1 1) 2 POINT (2 2) 3 POINT (3 3) 4 POINT (4 4) 5 POINT (5 5) 6 POINT (6 6) 7 POINT (7 7) 8 POINT (8 8) 9 POINT (9 9) dtype: geometry >>> s.sindex.intersection(box(1, 1, 3, 3).bounds) array([1, 2, 3]) Alternatively, you can use ``query``: >>> s.sindex.query(box(1, 1, 3, 3)) array([1, 2, 3]) """ # TODO: we should deprecate this # convert bounds to geometry # the old API uses tuples of bound, but Shapely uses geometries try: iter(coordinates) except TypeError: # likely not an iterable # this is a check that rtree does, we mimic it # to ensure a useful failure message raise TypeError( "Invalid coordinates, must be iterable in format " "(minx, miny, maxx, maxy) (for bounds) or (x, y) (for points). " "Got `coordinates` = {}.".format(coordinates) ) # need to convert tuple of bounds to a geometry object if len(coordinates) == 4: indexes = self._tree.query(shapely.box(*coordinates)) elif len(coordinates) == 2: indexes = self._tree.query(shapely.points(*coordinates)) else: raise TypeError( "Invalid coordinates, must be iterable in format " "(minx, miny, maxx, maxy) (for bounds) or (x, y) (for points). " "Got `coordinates` = {}.".format(coordinates) ) return indexes @property def size(self): """Size of the spatial index Number of leaves (input geometries) in the index. Examples -------- >>> from shapely.geometry import Point >>> s = geopandas.GeoSeries(geopandas.points_from_xy(range(10), range(10))) >>> s 0 POINT (0 0) 1 POINT (1 1) 2 POINT (2 2) 3 POINT (3 3) 4 POINT (4 4) 5 POINT (5 5) 6 POINT (6 6) 7 POINT (7 7) 8 POINT (8 8) 9 POINT (9 9) dtype: geometry >>> s.sindex.size 10 """ return len(self._tree) @property def is_empty(self): """Check if the spatial index is empty Examples -------- >>> from shapely.geometry import Point >>> s = geopandas.GeoSeries(geopandas.points_from_xy(range(10), range(10))) >>> s 0 POINT (0 0) 1 POINT (1 1) 2 POINT (2 2) 3 POINT (3 3) 4 POINT (4 4) 5 POINT (5 5) 6 POINT (6 6) 7 POINT (7 7) 8 POINT (8 8) 9 POINT (9 9) dtype: geometry >>> s.sindex.is_empty False >>> s2 = geopandas.GeoSeries() >>> s2.sindex.is_empty True """ return len(self._tree) == 0 def __len__(self): return len(self._tree)