h,SSKJr SSKJr SSKrSSKJrJrJrJ r J r J r SSK r SSK r SSKJrJrJrJr S/r\"S5r\ "S\\5r"S S \ R.\ \5r"S S \\5r"S S\\5r"SS\ \5rSSjrSSjrSSjrSSjrSSjr SSjr!SSjr"SSjr#g) ) annotations) itemgetterN)IteratorCallableSequenceIterableTypeVarGeneric) BoundingBoxVec2Vec3spherical_envelopeRTreeinfTc2\rSrSrSrS Sjr\RS Sj5r\RSSj5r \RSSj5r \RS\ 4SSjj5r \RSS j5r \RSS j5rSS jrSrg)NodebboxcXlgNrselfrs B/var/www/html/env/lib/python3.13/site-packages/ezdxf/math/rtree.py__init__ Node.__init__s!% cgrrs r__len__ Node.__len__ s!rcgrr r!s r__iter__ Node.__iter__#s'*rcgrr rpoints rcontains Node.contains&s*-rNcgrr )rtargetnnnn_dists r_nearest_neighborNode._nearest_neighbor)srcgrr rcenterradiuss rpoints_in_sphereNode.points_in_sphere.sILrcgrr rs rpoints_in_bboxNode.points_in_bbox1s@Crc$URU5$r)r0rr-s rnearest_neighborNode.nearest_neighbor4s%%f--r)rr returnintr@ Iterator[T]r)rr@boolr-rr.rr/floatr@tuple[T, float]r4rr5rGr@rCrr r@rCr-rr@rH)__name__ __module__ __qualname____firstlineno__ __slots__rabcabstractmethodr"r%r*INFr0r6r9r=__static_attributes__r rrrrsI& !!**--!%05  LLCC.rrc~^\rSrSrSrS U4SjjrSrS SjrSSjrS\ 4SSjjr SS jr SS jr S r U=r$)LeafNode8)pointsrcj>[U5Ul[TU] [ UR55 gr)tuplerXsuperrr )rrX __class__s rrLeafNode.__init__;s$Fm  T[[12rc,[UR5$r)lenrXr!s rr"LeafNode.__len__?s4;;rc,[UR5$r)iterrXr!s rr%LeafNode.__iter__BsDKK  rcB^[U4SjUR55$)Nc3F># UHnTRU5v M g7fr)isclose.0pr)s r $LeafNode.contains..Fs9[5==##[!)anyrXr(s `rr*LeafNode.containsEs9T[[999rNcZ^[U4SjUR55upEXC:aXTp2X#4$)Nc3J># UHnTRU5U4v M g7frdistance)rhrir-s rrj-LeafNode._nearest_neighbor..KsK{!vq115{s #)minrX)rr-r.r/rrr)s ` rr0LeafNode._nearest_neighborHs.Kt{{KK  {rc4^^UU4SjUR5$)Nc3V># UHnTRU5T::dMUv M g7frrq)rhrir4r5s rrj,LeafNode.points_in_sphere..Qs#G;a&//!*<*F;s) )rXr3s ``rr6LeafNode.points_in_spherePsG4;;GGrc0^U4SjUR5$)Nc3X># UHnTRU5(dMUv M! g7fr)inside)rhrirs rrj*LeafNode.points_in_bbox..Ts9;a$++a.;s* *ryrs `rr9LeafNode.points_in_bboxSs94;;99rry)rXlist[T]rBrDrFrIrJrLrMrNrOrPrr"r%r*rSr0r6r9rT __classcell__r\s@rrVrV8sX"I3 !:"&05 H::rrVc^\rSrSrSrS U4SjjrS SjrSSjrSSjrS\ 4SSjjr SS jr SS jr S r U=r$) InnerNodeW)childrenrc>[TU][55 [U5UlURHHnUR R UR RUR R/5 MJ gr) r[rr rZrrextendextminextmax)rrchildr\s rrInnerNode.__init__ZsV 'h ]]E II  ejj//1B1BC D#rc:[SUR55$)Nc38# UHn[U5v M g7fr)r_)rhcs rrj$InnerNode.__len__..bs1=a3q66=s)sumrr!s rr"InnerNode.__len__as14==111rc#\# URHn[U5ShvN M gN 7fr)rrb)rrs rr%InnerNode.__iter__ds#]]EE{ " "# "s ,* ,cURH=nURRU5(dM%URU5(dM= g g)NTF)rrr}r*)rr)rs rr*InnerNode.containshs;]]Ezz  ''ENN5,A,A#rNc$[URU5nURXU5up#URHTnXTLaM [URU5R U5(dM5URXU5upgXs:dMPUnUnMV X#4$r)find_closest_childrr0grow_boxrr})rr-r.r/ closest_childrr)rrs rr0InnerNode._nearest_neighborns+4==&A #55f'J ]]E% G,33F;;"'"9"9&g"N%B&G#{rc## URHan[[U5X#RRURR 5(dMHUR X5ShvN Mc gN 7fr)ris_sphere_intersecting_bboxr rr4sizer6)rr4r5rs rr6InnerNode.points_in_sphere~sY]]E*V fjj&7&7!11&AAA #BsAA7A7+A5, A7c## URH>nURUR5(dM%URU5ShvN M@ gN 7fr)r has_overlaprr9)rrrs rr9InnerNode.points_in_bboxs?]]E ++ //555#5s.AAA A)r)rSequence[Node[T]]r?rBrDrFrIrJrrs@rrrWsY$IE2#"&05  B66rrc\rSrSrSrSrSSSjjrSrSSjrSSjr SSjr SS jr SS jr SSS jjr SSS jjrSSS jjrSrg)raImmutable spatial search tree loosely based on `R-trees`_. The search tree is buildup once at initialization and immutable afterwards, because rebuilding the tree after inserting or deleting nodes is very costly and makes the implementation very complex. Without the ability to alter the content the restrictions which forces the tree balance at growing and shrinking of the original `R-trees`_, are ignored, like the fixed minimum and maximum node size. This class uses internally only 3D bounding boxes, but also supports :class:`Vec2` as well as :class:`Vec3` objects as input data, but point types should not be mixed in a search tree. The point objects keep their type and identity and the returned points of queries can be compared by the ``is`` operator for identity to the input points. The implementation requires a maximum node size of at least 2 and does not support empty trees! Raises: ValueError: max. node size too small or no data given .. _R-trees: https://en.wikipedia.org/wiki/R-tree )_rootcUS:a [S5e[U5n[U5S:Xa [S5e[X2[5Ulg)Nzmax node size must be > 1rzno points given) ValueErrorlistr_ make_node box_splitr)rrX max_node_size_pointss rrRTree.__init__sE 1 89 9v, w<1 ./ /wyA rc,[UR5$)z/Returns the count of points in the search tree.)r_rr!s rr" RTree.__len__s4::rc#J# [UR5ShvN gN7f)z%Yields all points in the search tree.N)rbrr!s rr%RTree.__iter__s ###s #!#c8URRU5$)zReturns ``True`` if `point` exists, the comparison is done by the :meth:`isclose` method and not by the identity operator ``is``. )rr*r(s rr*RTree.containsszz""5))rc8URRU5$)zWReturns the closest point to the `target` point and the distance between these points. )rr=r<s rr=RTree.nearest_neighborszz**622rc8URRX5$)zZReturns all points in the range of the given sphere including the points at the boundary. )rr6r3s rr6RTree.points_in_sphereszz**6::rc8URRU5$)z`Returns all points in the range of the given bounding box including the points at the boundary. )rr9rs rr9RTree.points_in_bboxszz((..rc[UR5Vs/sH,n[URRR 5PM. nn[ X15$s snf)zReturns the average size of the leaf bounding boxes. 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D) $ $]]E$U+ + +# % +sA AA Ac ^[USS5VV^s/sH unm[U4SjXS-S55PM" snn$s snnf)zRBrute force calculation of nearest neighbor distances with a complexity of O(n!). Nc3F># UHnTRU5v M g7frrqrgs rrj-nearest_neighbor_distances..Fs ;':!ENN1  ':rlr) enumeratert)rXrr)s `rrrAsM &fSbk2 2LE5 ;vaik': ;;2  s'?)rXrrrArz(Callable[[list[T], int], Sequence[Node]]r@Node[T])rXrrrAr@r) rr r5rGr4r rr r@rE)rrr)rr@r)rr rrGr@r )r list[float]rrGr@rG)rrr@zIterable[LeafNode[T]])rXz Sequence[T]r@r)$ __future__roperatorrrtypingrrrrr r rQr ezdxf.mathr r r r__all__rGrSrABCrrVrrrrrrrrrrr rrr s#KK BB ) El Ct.377GAJ.<:tAw:>16Q16hk=GAJk=\   =     ! +/ 7;   ' ,r