a 1$eݍ@s$ddlZddlmZddlZddlZddlmZ ddl m Z ddZ Gdd d Z e jrddlZdd lmZdd lmZGd d d ejje ZGdddejjZe jse jr ddlmZddlmZe jrddlZddej j!DdhBZ"n ddl#Zddej j!DdhBZ"Gddde Z$dS)N) BaseGeometry)_compat)doccCs&tjs tjrtStjrtStddS)zDynamically chooses a spatial indexing backend. Required to comply with _compat.USE_PYGEOS. The selection order goes PyGEOS > RTree > Error. zwSpatial indexes require either `rtree` or `pygeos`. See installation instructions at https://geopandas.org/install.htmlN)compatUSE_SHAPELY_20Z USE_PYGEOSPyGEOSSTRTreeIndex HAS_RTREE RTreeIndex ImportErrorr r L/var/www/html/django/DPS/env/lib/python3.9/site-packages/geopandas/sindex.py_get_sindex_class s rc@sVeZdZeddZdddZdddZdd d Zd d ZeddZ eddZ dS)BaseSpatialIndexcCstdS)aReturns valid predicates for this 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 {'contains', 'crosses', 'intersects', 'within', 'touches', 'overlaps', None, 'covers', 'contains_properly'} NNotImplementedErrorselfr r r valid_query_predicatessz'BaseSpatialIndex.valid_query_predicatesNFcCstdS)a 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)``. 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 or PyGEOS geometries. predicate : {None, "contains", "contains_properly", "covered_by", "covers", "crosses", "intersects", "overlaps", "touches", "within"}, 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). 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 (0.00000 0.00000) 1 POINT (1.00000 1.00000) 2 POINT (2.00000 2.00000) 3 POINT (3.00000 3.00000) 4 POINT (4.00000 4.00000) 5 POINT (5.00000 5.00000) 6 POINT (6.00000 6.00000) 7 POINT (7.00000 7.00000) 8 POINT (8.00000 8.00000) 9 POINT (9.00000 9.00000) 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 ((4.00000 2.00000, 4.00000 4.00000, 2.... 1 POLYGON ((6.00000 5.00000, 6.00000 6.00000, 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]]) 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. Nrrgeometry predicatesortr r r query/spzBaseSpatialIndex.querycCstdS)u DEPRECATED: use `query` instead. Returns all combinations of each input geometry and geometries in the tree where the envelope of each input geometry intersects with the envelope of a tree geometry. 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 envelope overlap or optional predicate are returned. When using the ``rtree`` package, this is not a vectorized function and may be slow. If speed is important, please use PyGEOS. Parameters ---------- geometry : {GeoSeries, GeometryArray, numpy.array of PyGEOS geometries} Accepts GeoPandas geometry iterables (GeoSeries, GeometryArray) or a numpy array of PyGEOS geometries. predicate : {None, "contains", "contains_properly", "covered_by", "covers", "crosses", "intersects", "overlaps", "touches", "within"}, 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 each 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, results sorted lexicographically using geometry's indexes as the primary key and the sindex's indexes as the secondary key. If False, no additional sorting is applied. Returns ------- ndarray with shape (2, n) The first subarray contains input geometry integer indexes. The second subarray contains tree geometry integer indexes. Examples -------- >>> from shapely.geometry import Point, box >>> s = geopandas.GeoSeries(geopandas.points_from_xy(range(10), range(10))) >>> s 0 POINT (0.00000 0.00000) 1 POINT (1.00000 1.00000) 2 POINT (2.00000 2.00000) 3 POINT (3.00000 3.00000) 4 POINT (4.00000 4.00000) 5 POINT (5.00000 5.00000) 6 POINT (6.00000 6.00000) 7 POINT (7.00000 7.00000) 8 POINT (8.00000 8.00000) 9 POINT (9.00000 9.00000) dtype: geometry >>> s2 = geopandas.GeoSeries([box(2, 2, 4, 4), box(5, 5, 6, 6)]) >>> s2 0 POLYGON ((4.00000 2.00000, 4.00000 4.00000, 2.... 1 POLYGON ((6.00000 5.00000, 6.00000 6.00000, 5.... dtype: geometry >>> s.sindex.query_bulk(s2) array([[0, 0, 0, 1, 1], [2, 3, 4, 5, 6]]) >>> s.sindex.query_bulk(s2, predicate="contains") array([[0], [3]]) Nrrr r r query_bulksGzBaseSpatialIndex.query_bulkTcCstdS)a Return the nearest geometry in the tree for each input geometry in ``geometry``. .. note:: ``nearest`` currently only works with PyGEOS >= 0.10. Note that if PyGEOS is not available, geopandas will use rtree for the spatial index, where nearest has a different function signature to temporarily preserve existing functionality. See the documentation of :meth:`rtree.index.Index.nearest` for the details on the ``rtree``-based implementation. 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 PyGEOS geometries} A single shapely geometry, one of the GeoPandas geometry iterables (GeoSeries, GeometryArray), or a numpy array of PyGEOS 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 (0.00000 0.00000) 1 POINT (1.00000 1.00000) 2 POINT (2.00000 2.00000) 3 POINT (3.00000 3.00000) 4 POINT (4.00000 4.00000) 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 (7.60000 7.60000) 1 POINT (10.00000 10.00000) dtype: geometry >>> s.sindex.nearest(s2) array([[0, 1], [8, 9]]) Nr)rr return_all max_distancereturn_distance exclusiver r r nearestsazBaseSpatialIndex.nearestcCstdS)a8Compatibility 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 (0.00000 0.00000) 1 POINT (1.00000 1.00000) 2 POINT (2.00000 2.00000) 3 POINT (3.00000 3.00000) 4 POINT (4.00000 4.00000) 5 POINT (5.00000 5.00000) 6 POINT (6.00000 6.00000) 7 POINT (7.00000 7.00000) 8 POINT (8.00000 8.00000) 9 POINT (9.00000 9.00000) 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]) Nrr coordinatesr r r intersectionMs$zBaseSpatialIndex.intersectioncCstdS)aSize 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 (0.00000 0.00000) 1 POINT (1.00000 1.00000) 2 POINT (2.00000 2.00000) 3 POINT (3.00000 3.00000) 4 POINT (4.00000 4.00000) 5 POINT (5.00000 5.00000) 6 POINT (6.00000 6.00000) 7 POINT (7.00000 7.00000) 8 POINT (8.00000 8.00000) 9 POINT (9.00000 9.00000) dtype: geometry >>> s.sindex.size 10 Nrrr r r sizesszBaseSpatialIndex.sizecCstdS)aCheck 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 (0.00000 0.00000) 1 POINT (1.00000 1.00000) 2 POINT (2.00000 2.00000) 3 POINT (3.00000 3.00000) 4 POINT (4.00000 4.00000) 5 POINT (5.00000 5.00000) 6 POINT (6.00000 6.00000) 7 POINT (7.00000 7.00000) 8 POINT (8.00000 8.00000) 9 POINT (9.00000 9.00000) dtype: geometry >>> s.sindex.is_empty False >>> s2 = geopandas.GeoSeries() >>> s2.sindex.is_empty True Nrrr r r is_emptyszBaseSpatialIndex.is_empty)NF)NF)TNFF) __name__ __module__ __qualname__propertyrrrrr"r#r$r r r r rs  r L c& r) RTreeError)prepcsxeZdZdZfddZeejfddZeejfddZe eej dd Z e eej d d Z Z S) SpatialIndexz9Original rtree wrapper, kept for backwards compatibility.cs tjdtddtj|dS)NzDirectly using SpatialIndex is deprecated, and the class will be removed in a future version. Access the spatial index through the `GeoSeries.sindex` attribute, or use `rtree.index.Index` directly. stacklevel)warningswarn FutureWarningsuper__init__)rargs __class__r r r3s zSpatialIndex.__init__cstj|g|Ri|SNr2r")rr!r4kwargsr5r r r"szSpatialIndex.intersectioncstj|i|Sr7)r2r)rr4r9r5r r rszSpatialIndex.nearestcCst|ddS)Nrr)lenleavesrr r r r#szSpatialIndex.sizecCst|dkrdS|jdkS)NrF)r:r;r#rr r r r$szSpatialIndex.is_empty) r%r&r'__doc__r3rrr"rr(r#r$ __classcell__r r r5r r+s  r+cseZdZdZfddZeeejddZeej ddd Z eej dd d Z dfd d Z eej fddZ eeej ddZ eeejddZddZZS)r zA simple wrapper around rtree's RTree Index Parameters ---------- geometry : np.array of Shapely geometries Geometries from which to build the spatial index. csdddt|D}zt|Wnty>tYn0||_tjdg|jjtd|_ dS)Ncss.|]&\}}t|r|js||jdfVqdSr7)pdZnotnullr$bounds).0iitemr r r sz&RTreeIndex.__init__..Zdtype) enumerater2r3r) geometriesnparrayr#object_prepared_geometries)rrstreamr5r r r3s zRTreeIndex.__init__cCshdS)N> overlapscontains_properly intersectsZtouchesNcoversZcrosseswithincontains covered_byr rr r r rsz!RTreeIndex.valid_query_predicatesNFc sjvrtdjtdrttsg}g}tD]6\}}j||d}||||gt |q@t ||gSdurt j gt j dSttstdtdjrt j gt j dSj} t| } | st j gt j dSdkr\g}| D]F} j| dur6tj| j| <j| r|| q|} n2durdvrxtfd d | D} |rt t j | t j dSt j | t j dS) Nz5Got `predicate` = `{}`, `predicate` must be one of {}Z __array__rrrDz0Got `geometry` of type `{}`, `geometry` must be za shapely geometry.rP)rQrNrRrOrMcs$g|]}tj|r|qSr )getattrrF)r@ index_in_treerrrr r Ssz$RTreeIndex.query..)r ValueErrorformathasattr isinstancerrErextendr:rGvstackrHZintp TypeErrortyper$r?listr"rJr*rFrQappendr) rrrrZ tree_indexZinput_geometry_indexrAZgeoresr?tree_idxrUr rVr rsf        zRTreeIndex.querycCs tjdtdd|j|||dSNzwThe `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.r,r-rSr/r0r1rrr r r ras zRTreeIndex.query_bulkrcs"tjdtddtj|||dS)u Returns the nearest object or objects to the given coordinates. Requires rtree, and passes parameters directly to :meth:`rtree.index.Index.nearest`. This behaviour is deprecated and will be updated to be consistent with the pygeos PyGEOSSTRTreeIndex in a future release. If longer-term compatibility is required, use :meth:`rtree.index.Index.nearest` directly instead. Examples -------- >>> s = geopandas.GeoSeries(geopandas.points_from_xy(range(3), range(3))) >>> s 0 POINT (0.00000 0.00000) 1 POINT (1.00000 1.00000) 2 POINT (2.00000 2.00000) dtype: geometry >>> list(s.sindex.nearest((0, 0))) # doctest: +SKIP [0] >>> list(s.sindex.nearest((0.5, 0.5))) # doctest: +SKIP [0, 1] >>> list(s.sindex.nearest((3, 3), num_results=2)) # doctest: +SKIP [2, 1] >>> list(super(type(s.sindex), s.sindex).nearest((0, 0), ... num_results=2)) # doctest: +SKIP [0, 1] Parameters ---------- coordinates : sequence or array This may be an object that satisfies the numpy array protocol, providing the index’s dimension * 2 coordinate pairs representing the mink and maxk coordinates in each dimension defining the bounds of the query window. num_results : integer The number of results to return nearest to the given coordinates. If two index entries are equidistant, both are returned. This property means that num_results may return more items than specified objects : True / False / ‘raw’ If True, the nearest method will return index objects that were pickled when they were stored with each index entry, as well as the id and bounds of the index entries. If ‘raw’, it will return the object as entered into the database without the rtree.index.Item wrapper. asindex.nearest using the rtree backend was not previously documented and this behavior is deprecated in favor of matching the function signature provided by the pygeos backend (see PyGEOSSTRTreeIndex.nearest for details). This behavior will be updated in a future release.r,r-) num_resultsobjects)r/r0r1r2r)rr!rfrgr5r r rks6 zRTreeIndex.nearestcstj|ddS)NF)rgr8r r5r r r"szRTreeIndex.intersectioncCs0t|dr|j}nt|dd}||_|S)N_sizerr)rZrhr:r;)rr#r r r r#s  zRTreeIndex.sizecCs|jjdkp|jdkSNr)rFr#rr r r r$szRTreeIndex.is_emptycCs|jSr7)r#rr r r __len__szRTreeIndex.__len__)NF)NF)rF)r%r&r'r<r3r(rrrrrrr"r#r$rjr=r r r5r r s&   ] C  r ) geoseries)rHcCsh|] }|jqSr namer@pr r r rpcCsh|] }|jqSr rlrnr r r rprqc@seZdZdZddZeddZeej ddd Z e d d Z eej dd d Z eej dddZ eejddZeeejddZeeejddZddZdS)rzA simple wrapper around pygeos's STRTree. Parameters ---------- geometry : np.array of PyGEOS geometries Geometries from which to build the spatial index. cCs0|}d|t|<t||_||_dSr7)copymodr$ZSTRtree_treerF)rrZ non_emptyr r r r3s zPyGEOSSTRTreeIndex.__init__cCstS)a$Returns valid predicates for the used 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", "intersects", "overlaps", "touches", "within"} )_PYGEOS_PREDICATESrr r r rsz)PyGEOSSTRTreeIndex.valid_query_predicatesNFcCs||jvr$td|d|j||}tjrF|jj||d}n.t|t j rd|jj ||d}n|jj||d}|r|j dkrt |S|\}}t ||f}t ||||fS|S)NzGot `predicate` = `{}`; z`predicate` must be one of {})rr)rrXrY_as_geometry_arrayrrrtrr[rGndarrayrndimrZlexsortr])rrrrindicesZgeo_idxrcZ sort_indexerr r r rs*     zPyGEOSSTRTreeIndex.querycCst|tjrt|}t|tjr.t|jSt|t j rB|j jSt|tj rT|jSt|t rht|S|durtdSt|trtdd|DSt|SdS)aConvert geometry into a numpy array of PyGEOS geometries. Parameters ---------- geometry An array-like of PyGEOS geometries, a GeoPandas GeoSeries/GeometryArray, shapely.geometry or list of shapely geometries. Returns ------- np.ndarray A numpy array of pygeos geometries. NcSs$g|]}t|trt|n|qSr )r[rrH_shapely_to_geom)r@elr r r rW=s z9PyGEOSSTRTreeIndex._as_geometry_array..)r[rsZGeometryrHZ_geom_to_shapelyrGrwZ from_shapely_datarkZ GeoSeriesvaluesZ GeometryArrayrrzr`Zasarray)rr r r rvs&          z%PyGEOSSTRTreeIndex._as_geometry_arraycCs tjdtdd|j|||dSrdrerr r r rGs zPyGEOSSTRTreeIndex.query_bulkTc Cs tjstjstd|r&tjs&td||}t|tsB|durH|g}tjrf|jj|||||d}n.|s|dur|s|j |S|jj |||d}|r|\}}n|}|stjst |dddf d} t | dd} |dd| f}|r|| }|r||fS|SdS)Nz8sindex.nearest requires shapely >= 2.0 or pygeos >= 0.10z:sindex.nearest exclusive parameter requires shapely >= 2.0)rrZ all_matchesr)rrrboolT)rrZ PYGEOS_GE_010rrvr[rrtZ query_nearestrZ nearest_allrGdiffZastypeinsert) rrrrrrresultryZ distancesmaskr r r rQsH      zPyGEOSSTRTreeIndex.nearestcCsz t|Wn ty,td|Yn0t|dkrN|jtj|}n.t|dkrn|jtj|}ntd||S)NzInvalid coordinates, must be iterable in format (minx, miny, maxx, maxy) (for bounds) or (x, y) (for points). Got `coordinates` = {}.r,) iterr^rYr:rtrrsboxZpoints)rr!indexesr r r r"s$     zPyGEOSSTRTreeIndex.intersectioncCs t|jSr7r:rtrr r r r#szPyGEOSSTRTreeIndex.sizecCst|jdkSrirrr r r r$szPyGEOSSTRTreeIndex.is_emptycCs t|jSr7rrr r r rjszPyGEOSSTRTreeIndex.__len__)NF)NF)TNFF)r%r&r'r<r3r(rrrr staticmethodrvrrr"r#r$rjr r r r rs2     )  9   r)%r/Zshapely.geometry.baserZpandasr>numpyrGrrZ _decoratorrrrr Z rtree.indexZrtreeZ rtree.corer)Zshapely.preparedr*indexIndexr+r Z SHAPELY_GE_20Z HAS_PYGEOSrkrHrZshapelyrsZstrtreeZBinaryPredicateruZpygeosrr r r r s4     !u