+ Ai0^RIt^RIHtHt]!RRRR7R4t]!RRRRR7R 4t!R R ]4tR t R#) N)njitprangezNi8(i8[:], i8[:], i8[:], i8[:], i8[:], f4[:, :, :], f4[:], f4[:, :], i8[:], i8)T)cachenogilc VP^,p \P!R\P4p ^p ^p ^V ,p^W&V ^, p VP^,W&V ^, p WV &V ^, p V ^, p V ^8EdV ^,p W,pV ^,p W,pV ^,p W,pW_^3,pW_^3,p\ V 4FpVVV3,VV&VVV3,VV&K! \ V^,V4FSp\ V 4FAp\ VV,VVV3,4VV&\ VV,VVV3,4VV&KC KU VV, V8:dVW&VW&RW/&RW?&RWO&RWo&EKVV, p\P!V4pVV,RVV,,,pTpV^, pVV8dVV8dVVV3,V8d V^, pK"VV8dVVV3,V8d V^,pK"VV8gKT\ V 4F%pVVV3,pVVV3,VVV3&VVVV3&K' VV,pVV,VV&VVV&KVVV3,V8d V^, pTp\ VV ,\ VV , V44pT pV ^, p T pV ^, p VW&V ^, p VW&V ^, p VW&V ^, p VW&V ^, p VW&V ^, p VW&V ^, p VW&VW&VW/&VW?&VWO&VWo&EKV #)g?)shapenpemptyint64rangeminmaxargmax)i0_indsi1_inds less_inds more_inds split_dimsbounds split_valuespointsindices min_leaf_size dimensionstack stack_sizen_nodes max_leaf_sizei_nodei1i0lohidilengths split_dim split_valuejtemp temp_i_nodei_splitlessmores&&&&&&&&&& S/var/www/html/photoedit/myenv/lib/python3.14/site-packages/pymatting/util/kdtree.py _make_treer4s QI HHT288 $EJG %ME!OJ QE!OJ* qLG!OJ q.a "a  a   AI  AI y!A2q5MBqE2q5MBqE" rAvr"A9%BqE6!Q$<01BqE6!Q$<01&# 7m # GO GO "I  "I !#J #&L 2gG '*IY-# 0B*BBKAQAa%!eq)| 4{ BFA!eq)| 4 CFAq5"9-%ad|'-ad|q!t '+q!t . #*!*K!(GAJ!,GAJal#k1QG"},c"}2Dg.NOGD qLGD qLG!#E  !OJ 'E  !OJ $E  !OJ!(E  !OJ "E  !OJ $E  !OJ GO GO $I  $I !*J #.L Nzgvoid(i8[:], i8[:], i8[:], i8[:], i8[:], f4[:, :, :], f4[:], f4[:, :], f4[:, :], i8[:, :], f4[:, :], i8))rrparallelc <VP^,p \VP^,4EFgp W,pW,pW,p\P!R\P4p^p^V^&^pV^8Ed V^,pVV,pVV 8dvRp\ V 4FMpVV,pV\ VV^V3,\VV^V3,V44, pVVV,, pKO VVV^, ,8dKVV,R8XEd\ VV,VV,4FpRp\ V 4F,pVV,VVV3,, pVVV,, pK. Tp\ V^, RR4FpVV,V8gKTpK VV 8gKx\ \VV ^, 4VR4F)pVV^, ,VV&VV^, ,VV&K+ VVV&VVV&\V^,V 4pK EKVV,pVV,pVV,p VV ,VV,8d VVV&V^, pVVV&V^, pEKVVV&V^, pVVV&V^, pEKV^, pEKj R#)rr r Nr )r rr rrrrr)!rrrrrrrr query_points out_indices out_distanceskri_query query_point distancesrr n_neighborsr r#distr(pdpr)distancedd insert_posr-r1r2r+s!&&&&&&&&&&&& r3 _find_knnrFs QI,,,Q/0"+ !* &rxx( a 1n !OJ:&Fay)A#AAS1 !5s6&!Q,;OQR7STTBBGOD*)K!O44&!R'wv@A"H"9-(^fQTl: BG+. "-J";?B;$Q<(2)*J< "A~!&s;A'> B!OA)0QGAJ+4QU+;IaL"P /0 +08 *-&)+/1&= /A4!( (&v. y)L,@@(,E*%!OJ(,E*%!OJ)-E*%!OJ(,E*%!OJ a _1r5c4a]tRt^toRtRRltRtRtVtR#)KDTreezKDTree implementationc  pVP\P8XgQhVPwr4^W2,^, V,,p\P!V\P 4Vn\P!V\P 4Vn\P!V\P 4Vn\P!V\P 4Vn \P!V\P 4Vn \P!V^V3\P4Vn \P!V\P4Vn VP4Vn\P!V4P!\P 4Vn\%VP VPVPVPVPVPVPVPVP"V4 VnR#)a3Constructs a KDTree for given data points. The implementation currently only supports data type `np.float32`. Parameters ---------- data_points: numpy.ndarray (of type `np.float32`) Dataset with shape :math:`n \times d`, where :math:`n` is the number of data points in the data set and :math:`d` is the dimension of each data point min_leaf_size: int Minimum number of nodes in a leaf, defaults to 8 Example ------- >>> from pymatting import * >>> import numpy as np >>> data_set = np.random.randn(100, 2) >>> tree = KDTree(data_set.astype(np.float32)) N)dtyper float32r rrrrrrrrrcopyshuffled_data_pointsarangeastypeshuffled_indicesr4r!)self data_pointsrn_datar max_nodess&&& r3__init__KDTree.__init__sh"  BJJ...'--&014FG xx 2884 xx 2884 )RXX6)RXX6((9bhh7hh 1i8"**E HHY ;$/$4$4$6! " & 1 8 8 B! LL LL NN NN OO KK     % %  ! !    r5c VP\P8XgQhVPP^,V8d.\ RVPP^, RV R24hVP^,p\P !W23\P4p\P !W23\P4p\VPVPVPVPVPVPVPVPVVVV4 VP V,p\P"!V4pWe3#)aQuery the tree Parameters ---------- query_points: numpy.ndarray (of type `np.float32`) Data points for which the next neighbours should be calculated k: int Number of neighbors to find Returns ------- distances: numpy.ndarray Distances to the neighbors indices: numpy.ndarray Indices of the k nearest neighbors in original data array Example ------- >>> from pymatting import * >>> import numpy as np >>> data_set = np.random.randn(100, 2) >>> tree = KDTree(data_set.astype(np.float32)) >>> tree.query(np.array([[0.5,0.5]], dtype=np.float32), k=3) (array([[0.14234178, 0.15879704, 0.26760164]], dtype=float32), array([[29, 21, 20]])) zNumber of data points (z4) is less than the number of neighbors requested (k=z<). Please provide a larger dataset or reduce the value of k.)rJr rKrMr ValueErrorrrrFrrrrrrrrPsqrt)rQr8r;n_querysquared_distancesrr>s&&& r3query KDTree.querys64!!RZZ///  $ $ * *1 - 1)$*C*C*I*I!*L)MNBCDBEEMM  $$Q'HHg\2::>((G<2 LL LL NN NN OO KK     % %     ''0GG-. !!r5) rrrrrr!rMrPrrN)) __name__ __module__ __qualname____firstlineno____doc__rUr\__static_attributes____classdictcell__) __classdict__s@r3rHrHs, \9"9"r5rHc:\V4pVPW4#)aFind k nearest neighbors in a data set. The implementation currently only supports data type `np.float32`. Parameters ---------- data_points: numpy.ndarray (of type `np.float32`) Dataset with shape :math:`n \times d`, where :math:`n` is the number of data points in the data set and :math:`d` is the dimension of each data point query_points: numpy.ndarray (of type `np.float32`) Data points for which the next neighbours should be calculated k: int Number of neighbors to find Returns ------- distances: numpy.ndarray Distances to the neighbors indices: numpy.ndarray Indices of the k nearest neighbors in original data array Example ------- >>> from pymatting import * >>> import numpy as np >>> data_set = np.random.randn(100, 2) >>> knn(data_set.astype(np.float32), np.array([[0.5,0.5]], dtype=np.float32), k=2) (array([[0.16233477, 0.25393516]], dtype=float32), array([[25, 17]])) )rHr\)rRr8r;trees&&& r3knnriYs6 + D ::l &&r5) numpyr numbarrr4rFobjectrHrir5r3rnsu V^bjno~p~Bow{DHSWX`X`Dj"Vj"Z'r5