+ LV-j%bRtRt.ROt^RIt^RIHt^RIt^RIH t ^RI H t ^RI H t HtHtHtHtHtHt^R IHtHtHtHt^R IHtHt^R IHtHtHtH t H!t!H"t"H#t#H$t$H%t%H&t&H't'H(t(^R I)H*t*H+t+^RI,t,!R R]]4t-Rt.Rt/Rt0Rt1Rt2Rt3RRlt4Rt5!RR]-]4t6!RR] ]-4t7R#)z1A sparse matrix in COOrdinate or 'triplet' formatzrestructuredtext en coo_array coo_matrixN)warn)copy_if_needed)spmatrix) coo_tocsr coo_todensecoo_todense_nd coo_matvec coo_matvec_ndcoo_matmat_densecoo_matmat_dense_nd)issparseSparseEfficiencyWarning_spbasesparray) _data_matrix _minmax_mixin) upcast_char to_nativeisshapegetdtypegetdatadowncast_intp_indexget_index_dtype check_shapecheck_reshape_kwargs isscalarlike isintlikeisdense)_validate_indices_broadcast_arraysc a]tRt^toRt]!^^A4tR4RR/Rllt]R4t ] PR4t ]R4t ] PR4t R t ] PP] nR5R lt] PP]nR5R lt] P P]nR tR6R lt] P$P]nV3RlRlt] P&P]nR7Rlt] P(P]nR8RltR8RltR8RltR8Rlt] P0P]nR8Rlt] P2P]nR8Rlt] P4P]nR9Rlt]P6P]nRtR:RltRtRt Rt!V3RlRlt"Rt#R t$R!t%R"t&R#t'R$t(R%t)R&t*R't+R(t,R)t-R*t.R;R+lt/R,t0R-t1R.t2R8R/lt3R0t4R1t5R2t6R3t7Vt8R#)< _coo_basecooNmaxprintc` aa a\P!WVR7S'g\o\V\4'Ed\ WP R7'd\WP R7VnVP\VP4R7o \V\R7p\;QJd3.V 3Rl\\VP444FNK 5M,!V 3Rl\\VP4444Vn\ P"!.VR7VnRVnEMVwrxTfr\,;QJdR T4F 'gK RM R M !R T44'd \+R 4h\;QJd.R T4FNK 5M !R T44p\Y P R7TnTPT\TP.4RR 7o \;QJd.TT 3RlT4FNK 5M!TT 3RlT44Tn\1TSTR7TnR TnEM\3V4'EdQVP4VP48XdS'd\;QJd.RVP4FNK 5M!RVP44VnVP$P7\W144Vn\VP.VP R7VnVP&VnEMVP9SR7p \ V P4VnV P$P7\W:4R R7Vn\V P.VP R7VnR VnEMd\ P:!V4p \V\<4'gA\ P>!V 4p V P@^8wd\)RV P@ R24h\V P.VP R7VnVeC\W P R7VP8wdRV RVP 2p \+V 4hVP\VP4R7oV PC4p\;QJd.V3RlV4FNK 5M!V3RlV44Vn\1W,SVR7VnRVn\VP4^8dF\;QJd.RVP4FNK 5M!RVP44VnVPE4R# \(\*3dp \)R4T hRp ? ii;i))r%allow_ndmaxval)defaultc3T<"TFp\P!.SR7xK R#5idtypeNnparray).0_ idx_dtypes& b/Volumes/fast/ai/experiments/ui-tars-smoke/.venv/lib/python3.14/site-packages/scipy/sparse/_coo.py %_coo_base.__init__..*s'$G.E%'HHRy$A$A.E%(r.Tzinvalid input formatNc3>"TFp\V4^8HxK R#5i)Nlenr3idxs& r6r7r85s;FS3s8q=FFz4cannot infer dimensions from zero sized index arraysc3"TF6p\P!\P!V44^,xK8 R#5iN)operatorindexr1maxr>s& r6r7r88s0"5-3c#+.."="A"A-3s>A)r*check_contentsc3V<"TFp\P!VSSR7xK R#5i)copyr/Nr0)r3r?rJr5s& r6r7r8>s($806%'HHSt9$M$M06s&)rIc3@"TFqP4xK R#5iNrJr>s& r6r7r8Es'Jks krMz!expected 2D array or matrix, not Dzinconsistent shapes: z != c3J<"TFpVPSRR7xK R#5iFrMN)astype)r3r? index_dtypes& r6r7r8_s($806%(JJ{J$G$G06 #c3b"TF%qP\PRR7xK' R#5irQ)rRr1int64r>s& r6r7r8es"XKS 288% @ @Ks-/)#r__init__r isinstancetupler _allow_ndr_shape_get_index_dtyperFrfloatranger=coordsr1r2datahas_canonical_format TypeError ValueErroranyshaperrformatrRtocooasarrayr atleast_2dndimnonzero_check)selfarg1rer/rJr% data_dtypeobjr_er$Mmessager5rSs&&&&f$ @@r6rW_coo_base.__init__ sd8<!D dE " "tnn55)$H  11T[[9I1J %eU; #e$G.3C 4D.E$Gee$G.3C 4D.E$GG HHRz: ,0)C"&KC=s;F;sss;F;;;(*>??!E"5-3"5EE"5-3"55E)%..I  11&9>"RDK040I0ID-**$*/C"' "3DK #0D5 QDI"-cii$.."QDK05D-JJt$!$00 a(Avv{'*KAFF8ST(UVV)!''DNNK $"5>>BdkkQ$9%T[[M"R(11"333t{{;K3L #e$806$8ee$806$88 #AIDF ,0) t{{ a %XDKKX%%XDKKXXDK m":.C#$:;BCsV V- V((V-cVP^8dVPR,#\P!VP4pVP RR7V#)rCF)write)rjr_r1 zeros_likecolsetflags)rmresults& r6row _coo_base.rowis@ 99q=;;r? "txx(e$ cVP^8d \R4h\P!WPR,P R7pVPRRV3,VPRR,VnR#)z8cannot set row attribute of a 1-dimensional sparse arrayr.Nrw)rjrcr1rhr_r/)rmnew_rows&&r6r|r}rs^ 99q=WX X**WKKO,A,ABkk#2&'3dkk"#6FF r~c(VPR,#)rCrr_)rms&r6ry _coo_base.colys{{2r~c\P!WPR,PR7pVPRRV3,VnR#)rCr.Nr)r1rhr_r/)rmnew_cols&&r6ryr}s7**WKKO,A,ABkk#2&'3 r~ca \WPVPR7p\V4wrEW0P8XdV'dVP 4#V#\ VP VPVR7p\V4^8Xd8VR8Xd\Wc^,4pM5\Wc^,4RRR1,pM\P!WcVR7pVPVP \V4R7o \;QJd.V 3RlV4FNK 5M!V 3RlV44pV'dVPP 4pM VPpVPW3VRR7#) r'orderCNr)c3T<"TFp\P!VSR7xK R#5ir-r1rh)r3cor5s& r6r7$_coo_base.reshape..sPZr2::b ::Zr9FrerJr)rrerZrrJ _ravel_coordsr_r=divmodr1 unravel_indexr\rFrYr` __class__) rmargskwargsrerrJ flat_coords new_coordsnew_datar5s &*, @r6reshape_coo_base.reshapesD**t~~F*62  JJ yy{" $DKK5I u:?|#Kq: #Kq:4R4@ ))+EJJ))$++c%j)I UPZPUUPZPP  yy~~'HyyH~~x4E~NNr~c.aVeV^8XEdVP^8Xd\VP4o\;QJd)V3RlVP4F 'gK RM RM!V3RlVP44'd \ R4hVPP^8wgO\;QJd&RVP4F 'gK RM RM!RVP44'd \ R4h\ S4#V^8dWP, pWP8d \ R4h\P!\VP^V, ,4VP^V, ,R7#) Nc3@<"TFp\V4S8gxK R#5irLr<)r3r?nnzs& r6r7$_coo_base._getnnz..s:ks3s8s?ksTFz3all index and data arrays must have the same lengthc3>"TFqP^8gxK R#5irB)rjr>s& r6r7rs)O;C((a-;r@z'coordinates and data arrays must be 1-Daxis out of bounds minlength) rjr=r`rdr_rcintr1bincountrre)rmaxisrs&&@r6_getnnz_coo_base._getnnzs .s&5(3JJs)<<(3r9zaxis z index z exceeds matrix dimension znegative axis z index: N)rjr=r_rc enumerater/kindrnamer\rFrerYrr`rmin)rmrr?r5s& @r6rl_coo_base._checks 99DKK( ($$' $4#5[ MN N  ,FAyy~~$|A3&>syy~~>NaP !#- ))$++c$**o)N e5(, 5ee5(, 55 dii( 88a<#DKK0779 1 -$uQCwswwykB99=A&IJJ779q=$~aS %LMM 1 r~cnaVf!\SP4RRR1,pM\S\4'dg\ VR4'd\ V4SP8wd \ R4h\ \V44SP8wd \ R4hMVR 8wd \ R4h\;QJd.V3RlV4FNK 5M!V3RlV44p\;QJd.V3RlV4FNK 5M!V3RlV44pSPSPV3W2R7#) N__len__z"axes don't match matrix dimensionszrepeated axis in transposezoSparse matrices do not support an 'axes' parameter because swapping dimensions is the only logical permutation.c3J<"TFpSPV,xK R#5irL)r[r3rrms& r6r7&_coo_base.transpose..s>+ V^9: :.sM6L(#CI6LsTFc34<"TF qS,xK R#5irLrr3r?rs& r6r7rs#E#II)rrC)rrZrjrcr=rr_remathprodr1rr`r[rrdzip logical_andreduceallrY) rmrermax_size tmp_shapetmp is_truncatingr?sizers &* @r6resize_coo_base.resizesENN; 99q==> > u:>@A A u: !' TZZ@Kyy'H**;y+A5IDK )8,DIK  u: ! OSZ!^,$))c%j012  ,,y)C**[c%j1DK))KSZ0DKMc$**e6LMMc$**e6LMM >>((,/ U,C*,Cys ,C*D88::#e#E#Eee#E#EE  IIdO  *s#J; c VPW4p\VPP4pV'g(VPP'g \ R4hVP ^8Xdg\\P!^.4VPVP VP^,VPVPR4V4EMUVP ^8XdXVPwrV\WVVPVP VP"VPVPR4V4MV'd:\P$!^\P&!VPRR44pMP\P$!\P&!VPR,RRR1,4RRR1,^4p\P(!VP4p\WpPVP WPVPR4V4VP+VP4#)z&Output array must be C or F contiguousANrCNNr)_process_toarray_argsrflags f_contiguous c_contiguousrcrjr r1r2rr_r`ravelrerr|ryappendcumprod concatenater) rmroutBfortranrrNstridesr_s &&& r6toarray_coo_base.toarray!s~  & &u 2agg**+qww333EF F 99> 288QC=$((DII;;q>499aggclG M YY!^::DA dhh$((DII g /))Arzz$**Sb/'BC))BJJtzz"~dd/C$DTrT$JAN^^DKK0F 7HHdii!99aggclG Eyy$$r~c VP^8wd\RVP R24hVP^8Xd(VPVPVP R7#^RIHpVPVP4wr4rVVPWTV3VR7pVP'gVP4V#)aConvert this array/matrix to Compressed Sparse Column format Duplicate entries will be summed together. Examples -------- >>> from numpy import array >>> from scipy.sparse import coo_array >>> row = array([0, 0, 1, 3, 1, 0, 0]) >>> col = array([0, 2, 1, 3, 1, 0, 0]) >>> data = array([1, 1, 1, 1, 1, 1, 1]) >>> A = coo_array((data, (row, col)), shape=(4, 4)).tocsc() >>> A.toarray() array([[3, 0, 1, 0], [0, 2, 0, 0], [0, 0, 0, 0], [0, 0, 0, 1]]) z+Cannot convert. CSC format must be 2D. Got rOr.) csc_arrayre) rjrcr_csc_containerrer/_cscr_coo_to_compressed_swaprar)rmrJrindptrindicesr`rexs&& r6tocsc_coo_base.tocsc<s( 99>J499+UVWX X 88q=&&tzz&D D '+/+B+B9??+S (FT##TF$;5#IA,,,  "Hr~c VP^8d\RVP R24hVP^8Xd(VPVPVP R7#^RIHpVPVPVR7pVwrErgVPWeV3VPR7pVP'gVP4V#)aConvert this array/matrix to Compressed Sparse Row format Duplicate entries will be summed together. Examples -------- >>> from numpy import array >>> from scipy.sparse import coo_array >>> row = array([0, 0, 1, 3, 1, 0, 0]) >>> col = array([0, 2, 1, 3, 1, 0, 0]) >>> data = array([1, 1, 1, 1, 1, 1, 1]) >>> A = coo_array((data, (row, col)), shape=(4, 4)).tocsr() >>> A.toarray() array([[3, 0, 1, 0], [0, 2, 0, 0], [0, 0, 0, 0], [0, 0, 0, 1]]) z*Cannot convert. CSR must be 1D or 2D. Got rOr.) csr_arrayrMr) rjrcr_csr_containerrer/_csrrrrrar) rmrJrarraysrrr`rers && r6tocsr_coo_base.tocsr]s( 99q=I$))TUVW W 88q=&&tzz&D D ',,Y__4,HF+1 (FT##TF$;4::#NA,,,  "Hr~c  V!VP4wr4VPVP\VPV4R7pVP ^8XdV'd"VP^,P 4MVP^,p\V4p\P!^V.VR7pV'dVPP 4M VPp WWP3#V!VP4wr\V 4pV PVRR7p V PVRR7p \P!V^,VR7p\P!WR7p\P!VPVPR7p \!W4WzWPWV 4 WWP3#)z?convert (shape, coords, data) to (indptr, indices, data, shape)r)r.FrM) _shape_as_2dr\r_rFrrjrJr=r1r2r`rerRempty empty_liker/r) rmswaprJrrrr5rrrr`majorminors &&& r6r_coo_base._coo_to_compressedsGD%%&))$++c$((A>N)O 99>/3dkk!n))+QGg,CXXq#hi8F'+499>>#DD**4 4DKK( %j YU 3 YU 3!a%y1--7}}TYYdjj9!E99ftLjj00r~c6V'dVP4#V#rLrM)rmrJs&&r6rg_coo_base.tocoos 99; Kr~cVP^8wd\RVP R24hVP4VPVP, p\ P !VRR7wr4\V4^d8d \R\V4 R2\^R7VPP^8Xd$\ P!R VPR7pMf\ P!\V4VPP4^,3VPR7pVPWTVP3&VPWS3VP R 7#) rz+Cannot convert. DIA format must be 2D. Got rOT)return_inversezConstructing a DIA matrix with z diagonals is inefficientrr.r)r;r;)rjrcrryr|r1uniquer=rrr`rzerosr/rF_dia_containerre)rmrJksdiagsdiag_idxr`s&& r6todia_coo_base.todias 99>J499+UVWX X  XX ))Bt< u:  23u:,?""(Q 8 99>>Q 88F$**5D88SZ)9:$**MD'+yyD488# $""D= "CCr~c~VP^8d\RVP R24hVP4VPVPVP R7pVP^8XdVP ^,pM\VP !p\\W0P44Vn V#)rz*Cannot convert. DOK must be 1D or 2D. Got rOr.) rjrcr_dok_containerrer/r_rdictr`_dict)rmrJdokr_s&& r6todok_coo_base.todoks 99q=I$))TUVW W !!$**DJJ!? 99>[[^F$++&FVYY/0  r~c  a VP^8wd \R4hVPwr#W)8:gW8d-\P!^VP P R7#\P!\V\V^4,V\V^4, 4VP R7pVPV,VP8Ho VP'd(VPS ,pVP S ,pMm\;QJd!.V 3RlVP4FNK 5M!V 3RlVP44pVPWpP S ,4wwrXpWdV\V^4,&V#)rz diagonal requires two dimensionsr.c34<"TF qS,xK R#5irLr)r3r? diag_masks& r6r7%_coo_base.diagonal..s?;CY;r)rjrcrer1r r`r/rrrFr|ryrarYr__sum_duplicates) rmkrowscolsdiagr|r`indsr4r's && @r6diagonal_coo_base.diagonals 99>?@ @ZZ  :88ATYY__5 5xxD3q!9,dSAY.>?"jj*XX\dhh.  $ $ $((9%C99Y'D5?4;;?55?4;;??D!11$ )8LMNHSd $S3q!9_ r~cVP^8wd \R4hVPwr4VP'd\V4'gR#VPP pVP VP, V8gpV^8d\W2,V4pVP'd\V\V44p\P!W`P V84p\P!V)V)V,VR7p \P!WuR7p M\W4V, 4pVP'd\V\V44p\P!W`PV84p\P!WuR7p \P!W"V,VR7p VP'dVRVp M%\P!WpP R7p WR&\P!VPV,V 34\P!VP V,V 343Vn \P!VPV,V 34VnRVnR#)rz*setting a diagonal requires two dimensionsNr.NNNF)rjrcrer=r|r/ryrr1 logical_oraranger rr_r`ra) rmvaluesr*rrrr5 full_keep max_indexkeeprrrs &&& r6_setdiag_coo_base._setdiags 99>IJ Jzz ;;;s6{{ HHNN HHtxx'1, q5AC I{{{ 3v;7 ==HH ,ABDiiQBN)DGii ;GAs I{{{ 3v;7 ==HH ,ABDii ;Giiy= BG ;;;jy)Hxx ? $)!r~c V'dB\;QJd.RVP4FNK 5M!RVP44pM VPpVPW3VPVPR7#)z|Returns a matrix with the same sparsity structure as self, but with different data. By default the index arrays are copied. c3@"TFqP4xK R#5irLrMr>s& r6r7'_coo_base._with_data.. s=#88::rNrer/)rYr_rrer/)rmr`rJr_s&&& r6 _with_data_coo_base._with_datasU U==UU===F[[F~~tnDJJdjj~QQr~c\WPVP4wr#rE\P!\ VP 4\PR7p.p.p.p \\W P44EF;wp wr\V \4'dWlV 8H,pK,\V \4'dV \R48XdVPV 4KeV PVPV ,4wrpV^8wdWV^8dW8*W8,pM W8W8,p\P !W, V4wppVV^8HV,,pMW8W8,pW, pVV,pVPV4EKVPV 4V PV 4EK> VR8Xd=VP V,P#4P%VP&RR7#VU u.uF qV,NK pp VP V,pV 'Ed>\)V !p V ^,Pp\P*!V 4P-\ V 4^R 4p\P*!VU u.uF qV,NK up 4R ,pVV8HP/^R7pVP14wppVV,pVU u.uF qV,NK pp \3\P4!VVR74p\ V4VR ,V^,, ^,8Xd!V^,pVRVV,VVR,pM VV,pV'dV'd\P6!V^,4pM<\P8!\ V4VP^,P&R7pVP;V^,V4VR,F#p VP;V VP=44K% \?VV3W0P&R7#uup iuup iuup i) r.NFrMrrrr>rrr2r2N) r rerfr1onesr=r`bool_rrr_rXrslicerrrsumrRr/r!r2rrrklistrrxrinsertrJr)rmkeyrE new_shape arr_int_posnone_pos index_mask slice_coords arr_coords arr_indicesrr?rstartstopstepin_rangenew_ixmrr arr_shapekeyarrfoundarr_coarr_ixnew_arr_coordspos coord_likes&& r6 __getitem___coo_base.__getitem__st2C T[[3 /+WWS^288<    %c%&=>LAy#s##Sy) C''%+% ''+(+ DJJqM(B%Eqy!8(*  'BH(*  'BH$&IIbj$$? "qAv&99 $&KBI#>!#"h.  ''/!!"%""3'-?0 ?99Z(,,.55djju5M M/;<|nn| <99Z( ;+[9K#A,,IXXk*223{3CQKFJ"GJbj>>J"GHTJz)..A.6E"]]_NFF'H/9:zV**zJ:!""2"26"KLN;;r?[^#Ca#GG!!n'->CDAQQ ,j8 ]]:a=9 XXc(m4;;q>;O;OP   hqk: 6b\\!!!Z__%67"(J/y SS]=,#H;sQQ+Q c ~\WPVP4wr4rVV'd;\V4pVRRR1,FpVP V4K \ V4pV'dg\V4pVUu/uF p\ W8,;p 4'dKWbK" p p\V P4!p \W4F wrWV&K \V4'd-^VP9dR#\W$VP4wrMA\P!W PR7pVP^8XdR#\!W$4wrVP#V4wr\%V 4^8Xd(\%V ^,4^8XdWuVnVnR#RpRpV'dVFp\+V\,4'dK\ V4'dK.VPp\%V4VR,V^,, ^,8Xd V^,pM^pV VV\%V4,p\/VV4pM ^p/p\1V4FEwppVV8XdV\%V4, p\+V\,4'gK7VVV&V^, pKG R.VP2,p\%V 4p\1V4Fwpp\ V4'd\P4!VV34VV&K4\+V\,4'dJVP7VPV,4wpppVV VV,,V,,VV&KVP94X,VV&K V P;4p\P<!V4p\P>!V^RR7wpp\P@!VVV,.4Vn\ ;QJd'.R\VVRV3,44FNK 5M !R\VVRV3,444VnRVn!R#uupi)Nr.T)r return_indexc3N"TFp\P!V4xK R#5irL)r1hstack)r3cs& r6r7(_coo_base.__setitem__..sV2UQBIIaLL2Us#%r2Fr)"r rerfrHpoprYrr!r5rr_get_sparse_data_and_coordsr/r1rhr_get_dense_data_and_coords _zero_manyr=r`r_rXrFrrrj broadcast_torrrJr2rrera)rmrJrrErKrLrMjrarrarr_posrQx_datax_coordsold_data old_coordsrXr^r? x_arr_coox_arr_coo_ravelx_axx_axesrnew_nnzrRrSrTrr4inds&&& r6 __setitem___coo_base.__setitem__esw2C T[[3 /+ YIdd^^ a $i(I KE'2U{!)58OC:Tvqv{GU+W^^-=>Kg3a4 A;;AGG|:1T FH 1JJ/Avv{9!G F $u5 x=A #hqk"2a"7%- "DIt{    !#u--inn # I ;'KOk!n,Lq,PQ)!n!)S3y>-A BI&3Iy&IO#(&FAsCxI&#u%% q   'Vdii' f+&FAs~~!#wj!A 1 C''$'KK 1 $>!tT!&&)):   %c%&=>LAy#s##Sy) C''C5;,>$'KK 1 $>!T19ax$&KBI#>$&KBI#>rz40A6X"55J "  :H*JC''C5;,>!!"%""3')?. XXk*223{3CQKFJ"GJbj>>J"GHTJ)..A.6EJGQ]]:6N?CN:--/27; < . (J48;;?;RZK; ?ii , M))#H@s J 4J%c<V^8dQhRR/#rr)rfrs"r6rrs ) ) )r~c VP'dR#VPVPVP4pVwVnVnRVnR#)zUEliminate duplicate entries by adding them together This is an *in place* operation NT)rar)r_r`)rmsummeds& r6r_coo_base.sum_duplicatessC  $ $ $ %%dkk499=!' TY$(!r~c aa\V4^8XdW3#\P!VRRR1,4o\;QJd.V3RlV4FNK 5M!V3RlV44pVS,p\PP VUu.uFq3R,VRR8gNK up4o\P !RS4o\;QJd.V3RlV4FNK 5M!V3RlV44p\P!S4wp\PPV\V4VPR7pW3#uupi)r;Nc34<"TF qS,xK R#5irLr)r3r?rs& r6r7,_coo_base._sum_duplicates..s4Vc5zzVrrTc34<"TF qS,xK R#5irLr)r3r? unique_masks& r6r7rs:6C;''6rr.r) r=r1lexsortrYr3rrrkaddreduceatrr/)rmr_r`r? unique_indsrrs&&& @@r6r)_coo_base._sum_duplicates s t9><  6$B$<(4V44V44E{mm**+1, +1CGs3Bx 6,  iik2 :6::6::zz+. vvt%8%ETZZX|, sEc aVP^8goVPS,Vn\;QJd(.V3RlVP4FNK 5VnR#!V3RlVP44VnR#)zKRemove zero entries from the array/matrix This is an *in place* operation c34<"TF qS,xK R#5irLrrs& r6r7,_coo_base.eliminate_zeros..%s=#IIrN)r`rYr_)rmrs&@r6eliminate_zeros_coo_base.eliminate_zerossM yyA~IIdO e==e e=== r~c VPVP8wd'\RVP RVP R24h\VPPVPP4p\ P !WRR7p\VPP4pVP^8Xdg\\ P !^.4VPVPVP^,VPVPR4V4EMUVP^8XdXVP wrV\#WVVPVP$VP&VPVPR4V4MV'd:\ P(!^\ P*!VPRR 44pMP\ P(!\ P*!VPR,RRR 1,4RRR 1,^4p\ P,!VP4p\WpPVPWPVPR4V4VP/VRR 7#) Incompatible shapes ( and rT)r/rJrNrFrMr)rercrr/charr1r2rrrrjr rr_r`rr rr|ryrrr _container) rmotherr/r{rrrrrr_s && r6 _add_dense_coo_base._add_dense+s ;;$** $4TZZLekk]RSTU UDJJOOU[[-=-=>%48fll//0 99> 288QC=$((DII;;q>499fll36G R YY!^$$DA dhh$((DII S)7 4))Arzz$**Sb/'BC))BJJtzz"~dd/C$DTrT$JAN^^DKK0F 7HHdii!99fll3.? JvE22r~c VP^8d VP4PV4#VPVP8wd'\ RVP RVP R24hVP V4p\ P!VPVP34p\\ P!VPVP3^R74pVP W#3VPR7pV#rrrrBr) rjr _add_sparserercrr1rr`rYr_rmrrrrs&& r6r_coo_base._add_sparseCs 99q=::<++E2 2 ;;$** $4TZZLekk]RSTU Uu%>>499ejj"9:2>>4;; *EANO NNH1N Dr~cVP^8d VP4PV4#VPVP8wd'\ RVP RVP R24hVP V4p\ P!VPVP)34p\\ P!VPVP3^R74p\W#3VPR7pV#r) rjr _sub_sparserercrr1rr`rYr_rrs&& r6r_coo_base._sub_sparseOs 99q=::<++E2 2 ;;$** $4TZZLekk]RSTU Uu%>>499uzzk":;2>>4;; *EANO x,DJJ ?r~c (VP^8EdK\P!\P!VP RR4\ VPPVPP4R7p\P!VP 4p\P!\P!VRRRRR1,4RRR1,R,^4p\P!VP4p\VP\!VP 4WEVP"W4VP%VP RR4pV#VP^8dVP ^,M^p\P!V\ VPPVPP4R7pVP^8XdVP&pVP(pMSVP^8Xd+VP^,p\P*!V4pM\-RVP 24h\/VPWVP"W4\1V\24'dV^8Xd V^,#V#)rNr.rz$coo_matvec not implemented for ndim=r)rjr1rrrrerr/rr2rrrr_r rr=r`rryr|rxNotImplementedErrorr rXr) rmrr{rerr_ result_shaperyr|s && r6_matmul_vector_coo_base._matmul_vector[s 99q=XXdii 3B8$/ AQAQ$RTFHHTZZ(Eii 5":dd+; ((C((C YY!^++a.C--$C%6tyykBD D 488Styy%@ dG $ $):!9  r~cb\V4'dVPV4#VPp\ \V4RR4\ \V4RRRRR1,4,pVPV4pVPp\ \V4RR4\ \V4RRRRR1,4,pVPV4PV4pV\Jd\#V^8XgV^8Xd\VP4pMQ\ \VP4RR4\ \VP4RRRRR1,4,pVPV4# \d'\P !T4pTPpEL}i;i)Nrwr) r _mul_scalarrjAttributeErrorr1rhrYr^r_matmul_dispatchNotImplemented)rmro_ndimpermtrs_ndimrets&& r6_rmatmul_dispatch_coo_base._rmatmul_dispatch|sa   ##E* * $vs+,uU6]235G"5M/NND&BYYFvs+,uU6]235G"5M/NND..&77;Cn$%%{fkSXXU388_Sb12U5?23;OPTRTPT;U5VV==& &!" $ 5) $s E==-F.-F.c\V4'dVPV4#\V4'gk\V4'gZ\P !V4pVP ^8Xd&VP\P8Xd\#VPVP ^8d(VP ^8d\P!W4#VPR ,pRpVP\PJEd9VPV38XdVP!V4#VPV^38XdBVP!VP#44pVP$!.VPRR O^N5!#VP ^8Xd(V RV RVP^, R2p\'V4hVPR ,V8XdMVPRR pVPRR pWx8wd\P(!Wx4VP+V4#\'V RV RVPR , R24h\V4'dVP-V4#\V4'EdVP ^8Hp VP ^8Hp V 'dVP%VP.4pV 'd%VP%VP^,^34pW1PR ,8wd&\'V RV RVPR , R24hVP ^8gVP ^8d<VPRR pVPRR pWx8wd\P(!Wx4VP1V4pV 'dEVP%\3VPRR 4\3VPR R4,4pV 'dVP%VPRR 4pV#R# \dTpELui;i \&d \'R4hi;i \&d \'R4hi;i) r;z)matmul: dimension mismatch with signatureNz (n,k=z),(k=z,)->(n,)z&Batch dimensions are not broadcastablez (n,..,k=z,..,m)->(n,..,m)rrw)rmultiplyrrr1 asanyarrayrjr/object_rrerrrrndarrayrrrrcbroadcast_shapes_matmul_multivectorrr _matmul_sparserY) rmrother_ar err_prefixr{msg batch_shape_A batch_shape_B self_is_1d other_is_1ds && r6r_coo_base._matmul_dispatchs   ==' '75>>mmE*G||q W]]bjj%@&%   99q=UZZ!^++D8 8 JJrN@ ??bjj ({{qd"**511{{q!f$,,U[[];~~:tzz#2:::zzQ# F1#U5;;q>2B(K o%{{2!# $ 3B % CR 0  1S++MI//66 !l)A3eEKKO3DDTU   ##E* * E??aJ**/K||D$5$56 u{{1~q&9:KKO# !l)A3eEKKO3DDTU yy1} Q $ 3B % CR 0  1S++MI((/Ffll3B.?(@(-fll23.?(@)AB Sb(9:MM M"  2&S()QRRSN&S()QRRSs* P6P&P1 PPP.1Qc \VPPVPP4pVP^8gVP^8EdVP^8XdwVP ^VP ^,4P V4pVP \VP RR4\VP RR4,4#\P!VP RRVP RR4pW@P RR,pWAP RR,pVPV4p\P!W4pW@P RR,VP RR,p\P!WrR7p\VP\VP 4VP R,\P !V4\P !V4\P"!VP$4VP&VP)R4V4 V#VP^8Xd@VP ^,VP ^,3pVP*pVP,p MNVP^8Xd>VP ^,3pVP$^,p\P.!V4p \P!XVR7p\1VPVP R,X XVP&VP)R4V4VP3\5V4R7#)rNr.r)typerwr)rr/rrjrrerrYr1r _broadcast_torlrr rr=r2rr_r`rryr|rxr viewr) rmr result_dtyper{broadcast_shape self_shape other_shaperryr|s && r6r_coo_base._matmul_multivectors\"4::??EKK4D4DE 99>UZZ1_yyA~aA7KKER~~eEKK,<&=ekkRTRUFV@W&WXX 11$**Sb/5;;sPRCSTO(::bc?:J)KK,<>RSAQQLXXl?F #djj/5;;r? " 5rxx 7M "t{{ ; $ 5;;s+;V EM 99> JJqM5;;q>:L((C((C YY!^!KKN,L++a.C--$C,l;5;;r?CEKK$4f >{{U {,,r~c \V4'g\V4'g}\V4'gl\P!V4pVP ^8Xd8VP \P8Xd\R\V4 R24hVP\V4'd W,#VPR,VPRR^,8wd'\RVP RVP R24hVP ^8dVP ^8d W,#\V4'dVPV4#VPVP44# \dTpLi;i) ahReturn the dot product of two arrays. Strictly speaking a dot product involves two vectors. But in the sense that an array with ndim >= 1 is a collection of vectors, the function computes the collection of dot products between each vector in the first array with each vector in the second array. The axis upon which the sum of products is performed is the last axis of the first array and the second to last axis of the second array. If the second array is 1-D, the last axis is used. Thus, if both arrays are 1-D, the inner product is returned. If both are 2-D, we have matrix multiplication. If `other` is 1-D, the sum product is taken along the last axis of each array. If `other` is N-D for N>=2, the sum product is over the last axis of the first array and the second-to-last axis of the second array. Parameters ---------- other : array_like (dense or sparse) Second array Returns ------- output : array (sparse or dense) The dot product of this array with `other`. It will be dense/sparse if `other` is dense/sparse. Examples -------- >>> import numpy as np >>> from scipy.sparse import coo_array >>> A = coo_array([[1, 2, 0], [0, 0, 3], [4, 0, 5]]) >>> v = np.array([1, 0, -1]) >>> A.dot(v) array([ 1, -3, -1], dtype=int64) For 2-D arrays it is the matrix product: >>> A = coo_array([[1, 0], [0, 1]]) >>> B = coo_array([[4, 1], [2, 2]]) >>> A.dot(B).toarray() array([[4, 1], [2, 2]]) For 3-D arrays the shape extends unused axes by other unused axes. >>> A = coo_array(np.arange(3*4*5*6)).reshape((3,4,5,6)) >>> B = coo_array(np.arange(3*4*5*6)).reshape((5,4,6,3)) >>> A.dot(B).shape (3, 4, 5, 5, 4, 3) z"dot argument not supported type: ''Nzshapes rz are not aligned for n-D dotrrw)rrrr1rrjr/rrbrrerrc _dense_dot _sparse_dotrg)rmro_arrays&& r6dot _coo_base.dots3l75>>\%5H5HmmE*G||q W]]bjj%@"DT%[MQR STT    <  ::b>U[[-a0 0wtzzl% };<= = 99q=UZZ!^<  5>>??5) ) ..!"  s E55 FFc\WP^, .4wr#\V\^VP^, 4.4wrEW$P,pVP 4pW5,p.pV'dW53MV3p \ VP V 4F*wrVP\P!W44K, \VPV3VR7#)rCr) _convert_to_2drjrFTrgrr_extendr1rrr`) rmrself_2d s_new_shapeother_2d o_new_shapercombined_shaper_ new_shapesrfss && r6r_coo_base._sparse_dotes .dYY]OD .us1ejj1n7M6N O#zz|%23>k/[N  Z0DA MM"**10 11$))V,NCCr~c|VPpV^8:d!V^8XdRMVP^,3pTpM\WP^, .4wrCVPpV^8:d!V^8XdRMVPR,3pTpMVPRRVPRR,pV^, .\V^, 4OV^, N5p\P !W4p V P VPR,\P!V434pWG,p W6,p V P V 4#)rNrrrw) rjrerr^r1rrrr) rmrrrrrrr reorder_dimso_reorgrrs && r6r_coo_base._dense_dotzs  Q; &! "$**Q-1AKG#1$Q#H G Q; &! "%++b/1CKH++cr*U[[-==K"QJGvz):GFQJGLll57G B;9O'PQH!%2||N++r~c aa\S4'g}\S4'gl\P!S4pVP^8Xd8VP \P 8Xd\R\S4 R24hSP\SPSPV4wrE\;QJd)VV3Rl\WE44F 'gK RM RM!VV3Rl\WE444'd \R4h\S4'dSPSWE4#SP!SWE4# \dToLi;i)a4 Return the tensordot product with another array along the given axes. The tensordot differs from dot and matmul in that any axis can be chosen for each of the first and second array and the sum of the products is computed just like for matrix multiplication, only not just for the rows of the first times the columns of the second. It takes the dot product of the collection of vectors along the specified axes. Here we can even take the sum of the products along two or even more axes if desired. So, tensordot is a dot product computation applied to arrays of any dimension >= 1. It is like matmul but over arbitrary axes for each matrix. Given two tensors, `a` and `b`, and the desired axes specified as a 2-tuple/list/array containing two sequences of axis numbers, ``(a_axes, b_axes)``, sum the products of `a`'s and `b`'s elements (components) over the axes specified by ``a_axes`` and ``b_axes``. The `axes` input can be a single non-negative integer, ``N``; if it is, then the last ``N`` dimensions of `a` and the first ``N`` dimensions of `b` are summed over. Parameters ---------- a, b : array_like Tensors to "dot". axes : int or (2,) array_like * integer_like If an int N, sum over the last N axes of `a` and the first N axes of `b` in order. The sizes of the corresponding axes must match. * (2,) array_like A 2-tuple of sequences of axes to be summed over, the first applying to `a`, the second to `b`. The sequences must be the same length. The shape of the corresponding axes must match between `a` and `b`. Returns ------- output : coo_array The tensor dot product of this array with `other`. It will be dense/sparse if `other` is dense/sparse. See Also -------- dot Examples -------- >>> import numpy as np >>> import scipy.sparse >>> A = scipy.sparse.coo_array([[[2, 3], [0, 0]], [[0, 1], [0, 5]]]) >>> A.shape (2, 2, 2) Integer axes N are shorthand for (range(-N, 0), range(0, N)): >>> A.tensordot(A, axes=1).toarray() array([[[[ 4, 9], [ 0, 15]], [[ 0, 0], [ 0, 0]]], [[[ 0, 1], [ 0, 5]], [[ 0, 5], [ 0, 25]]]]) >>> A.tensordot(A, axes=2).toarray() array([[ 4, 6], [ 0, 25]]) >>> A.tensordot(A, axes=3) array(39) Using tuple for axes: >>> a = scipy.sparse.coo_array(np.arange(60).reshape(3,4,5)) >>> b = np.arange(24).reshape(4,3,2) >>> c = a.tensordot(b, axes=([1,0],[0,1])) >>> c.shape (5, 2) >>> c array([[4400, 4730], [4532, 4874], [4664, 5018], [4796, 5162], [4928, 5306]]) z#tensordot arg not supported type: 'rc3v<"TF.wrSPV,SPV,8gxK0 R#5irLr)r3axbxrrms& r6r7&_coo_base.tensordot..s/9762zz"~R07s69TFz*sizes of the corresponding axes must match)rrr1rrjr/rrbrrer _process_axesrdrrc_dense_tensordot_sparse_tensordot)rmrr other_array axes_self axes_othersff& r6 tensordot_coo_base.tensordotsru~~huoo--.K1$):):bjj)H"Ed5k]RS TUU $ !.diiT J  39 793339 79 9 9IJ J 5>>(( F F))%G G" $# $s E EEc\W4wrE\W4wrgWFP,p\V4'gV#VP4pWW,p .p V'dWW3MV3p \ VP V 4F4wrV 'gKV P \P!W44K6 \VPV 3V R7#)r) rrrrgrr_rr1rrr`)rmrs_axeso_axesrrrrrrr_rrfrs&&&& r6r_coo_base._sparse_tensordots .d; .u =#~~Kzz|%23>k/[N  Z0DAq b..q451 $))V,NCCr~c~\VP4p\VP4p\V4Uu.uF qfV9gK VNK ppVUu.uFq`PV,NK ppVUu.uFq`PV,NK p p\V4Uu.uF qfV9gK VNK p pVUu.uFqaPV,NK p pV Uu.uFqaPV,NK p pVPWr,4p \P!WRRV,V RR,4p.V O\ P !V4N5p.V RRO\ P !V 4NV RRO5pV PV4PVPV44#uupiuupiuupiuupiuupiuupi)Nr) r=rer^rr1rrrr)rmrrrrrr s_non_axes s_axes_shapes_non_axes_shape o_non_axes o_axes_shapeo_non_axes_shapeleftright reshape_left reshape_rights&&&& r6r_coo_base._dense_tensordotsTZZU[[!!&vBA6/aa B/56v! 1 v 63=>:aJJqMM:>!&vBA6/aa B0671 A 74>?JqKKNNJ?~~j12 UsOf$B7?s/F!F!F&2F+F0)F05F5F:c 8VP^8d(VP^8d\P!W4#VPpVPp\P !VRRVRR4p\ V4VRR,p\ V4VRR,pVPV4pVPV4p\V4p \V4p W,P4p \V .VOVPR,NVPR,N5R7#)a Perform sparse-sparse matrix multiplication for two n-D COO arrays. The method converts input n-D arrays to 2-D block array format, uses csr_matmat to multiply them, and then converts the result back to n-D COO array. Parameters: self (COO): The first n-D sparse array in COO format. other (COO): The second n-D sparse array in COO format. Returns: prod (COO): The resulting n-D sparse array after multiplication. Nrrwr) rjrrrer1rrYr _block_diagrg_extract_block_diag) rmrrrrself_new_shapeother_new_shapeself_broadcastedother_broadcastedself_block_diagother_block_diagprod_block_diags && r6r_coo_base._matmul_sparse4s  99q=UZZ!^))$6 6ZZ kk --j"o{3B?OP/*RS/A0;rs3CC--n=!//@&&67&'89+=DDF# EOETZZ^EU[[_E  r~c \VPV8XdV'dVP4#T#VPp\V4\V48d \R4hR \V4\V4, ,\ V4,p\ ;QJd%R\ WA44F 'gK RM RM!R\ WA444'd\RV R24hVPV4p\VP\V4R7pVPpVPpVR Rp^p VR ,VR ,8wdbVR ,p W,p \P!Wz4p\P!\P!^WR7VP 4p V 3p\#R \V4^,)R 4Fp WL,W,8wdW,p W,p \V4p \P!Wz4p\ \P!W^,RV 44p\P!\P!^WR7V 4p V 3V,pK\P!Wl,V 4p V 3V,pK \%Wx3V4#) zDNew shape must have at least as many dimensions as the current shapec3H"TFwrV^8g;'dW8gxK R#5irBr)r3ons& r6r7*_coo_base._broadcast_to..ms%E/DtqQ!!16!/Ds" "TFzcurrent shape z- cannot be broadcast to new shape {new_shape}r)Nr.rrrw)rerJr=rcrYrdrrrr_rFr`r1tilerepeatr4rr^r)rmrKrJ old_shaperer5r_rr cum_repeat repeat_countnew_dimrrs&&& r6r_coo_base._broadcast_to^s% :: ""&499; 0D 0JJ  y>C N *56 6IY785;KK 3Es5/DE333Es5/DE E E~i[9BBC C||E"#DKKIG 99BC[  9 " %$R=L  &Jwwx6Hii !\ KTXXVG!JrSZ\?B/Ax9<'(| * (m778:"277:cd+;\#JK ))BIIa$OQTU%Z*4 ''&)Z8%Z*4 !0$(/;;r~c\W4wrE\P!VP^,^3VR7pWF,P V4VR&V#)rCr..)rr1rDrer)rmr res_dtyperA2drKrDs&&&& r6_sum_nd_coo_base._sum_ndsF'3ww ! a( :J'' 2C r~c\W4wrEVP^W#4p\P!VP^,V4p\ VP V3V4#r)r_min_or_max_axisr1rr_rr`)rmr min_or_maxexplicitrrKresunraveled_coordss&&&& r6_min_or_max_axis_nd_coo_base._min_or_max_axis_ndsR'3""1j;++CJJqM9E#(($45yAAr~cd\W4wrVVP^W#V4pVPV4#r)r_argminmax_axisr)rmr argminmaxcomparer$rrKres_flats&&&&& r6_argminmax_axis_nd_coo_base._argminmax_axis_nds3'3&&q)hG **r~)r[r_r`ra)NNFrL)NF)NN)F)r;)T)r)9__name__ __module__ __qualname____firstlineno___formatr^rZrWpropertyr|setterryrr__doc__rrrlrrrrrrrgrr#r/rr9r?r`rzrkrr)rrrrrrrrrrrrrrrrrr'r.__static_attributes____classdictcell__rs@r6r#r#s;Ga IGTGR ZZGG  ZZ44!OFoo--GO;(oo--GO W$1199MN2?$ ))11I$$L^^++FN%2oo--GOB D16 MM))EMD,MM))EM MM))EM($,,44H$*NRRThn*`)*V ) )&>30  B'2[z!-FN/`D*,6nH`D4L*( T5r=r`r|ryrtemp_block_idxrrs && r6rrs9eBi5 99Dxx3u:txx02r *x&- '/+ dE*-.e <&K' W,p K \P"!VP%4V 4pWC3#)FrMrNzshape mismatch in assignmentr)rgrrHr_r`rRrer=r1 zeroslikerYr^rcrrrr4rr)rrKr/rqrpx_shapelen_diff coord_zerosr4 tot_expandrnnnxx_nnzrmrs&&& r6riris  AAHH~H VV]]5u] -FggG9~G ,H!|#.4=0ll8A;/ +1H<=!|y!AqzQ!"+#B<!!#2,#CR= !?@@"J Y!89 8B 8  7;< <HQK ii " u5 x(EAv''"/HK)  :WWV\\^Z 0F  r~c:VPV8wd&\P!VP4V4pVR8XdE\ \P !^.4.\ V4,4pVP4pW23#VP4pW,pW23#)r;r) rer1rlsqueezerYr2r=rrk)rrKrqrps&& r6rjrj s}ww) OOAIIK 3B"((A3-3y>9:  99;  r~cjaa\V\4'dUV^8gV\SS48d \R4h\ \ SV, S44p\ \ V44pM\V\ \,4'd\V4^8wd \R4hVwr4\V4\V48wd \R4h\;QJdV3RlV4F 'gK RM RM!V3RlV44'gA\;QJdV3RlV4F 'gK RM RM!V3RlV44'd \R4hM \R 4hVUu.uFqU^8d VS,MTNK ppVUu.uFqU^8d VS,MTNK ppW43#uupiuupi) rCz.axes integer is out of bounds for input arraysz%axes must be a tuple/list of length 2z,axes lists/tuples must be of the same lengthc3H<"TFqS8;'gVS)8xK R#5irLr)r3rndim_as& r6r7 _process_axes..&$=fV|++rVG|+f""TFc3H<"TFqS8;'gVS)8xK R#5irLr)r3rndim_bs& r6r7rP'rQrRz/axes indices are out of bounds for input arraysz3axes must be an integer or a tuple/list of integers) rXrrrcrHr^rYr=rdrb)rOrTraxes_aaxes_brsff& r6rrsS$ !8tc&&11MN NeFTM623eDk" D%$, ' ' t9>DE E v;#f+ %KL L 3=f=333=f= = = 3=f=333=f= = =NO O >MNN>D EfdaxdVmT1fF E>D EfdaxdVmT1fF E >F Es ,F+ F0cTaa\;QJd.V3RlS4FNK 5M!V3RlS44p\;QJd.V3RlS4FNK 5M!V3RlS44p\W#4p\SP4p\;QJd .V3Rl\ V44FNK 5M!V3Rl\ V444pV'd\;QJd.V3RlV4FNK 5M!V3RlV44p\;QJd.V3RlV4FNK 5M!V3RlV44p\Wx4p W3p \ P !V4\ P !V43p MV3p \ P !V43p Rp\SPV 3V R7p W3#)c3J<"TFpSPV,xK R#5irLrr3rr$s& r6r7!_convert_to_2d..2s4t! 1 trTc3J<"TFpSPV,xK R#5irLrrYs& r6r7rZ3s2Tsyy||TrTc38<"TFqS9gK VxK R#5irLr)r3rrs& r6r7rZ7s= 1}QQ s c3J<"TFpSPV,xK R#5irLrrYs& r6r7rZ9s@x! 1 xrTc3J<"TFpSPV,xK R#5irLrrYs& r6r7rZ:s>Xsyy||XrTrr) rYrr=r_r^rrrr`) r$r axis_coords axis_shape axis_ravelrjnon_axisnon_axis_coordsnon_axis_shapenon_axis_ravel coords_2dshape_2dnew_coos ff r6rr1s$%4t4%%4t44K2T22T22J{7J szz?Du=d =uu=d ==H%@x@%%@x@@>X>>X>>&G#0 IIn-tyy/DEM IIj)+9-X>G  ""r~c:\V4^8Xd V^,#\V4^8XdVwr4VwrVVR8Xd^V\^V^, 4,\^V^, 4,p\VR7p\P!WEVR7V,#VR8Xd^V\^V^, 4,\^V^, 4,p\VR7p\P!W6VR7V,#\ R4h\P !WVR7#)z;Like np.ravel_multi_index, but avoids some overflow issues.rr)r.Fz'order' must be 'C' or 'F'r)r=rFrr1rrcravel_multi_index) r_rernrowsncolsr|ryr*r5s &&& r6rrGs 6{aay 6{a  C<c!UQY//#a2CCF'v6I;;u;cA A c\c!UQY//#a2CCF'v6I;;u;cA A9: :  U ;;r~c"\V\4#)aIs `x` of coo_matrix type? Parameters ---------- x object to check for being a coo matrix Returns ------- bool True if `x` is a coo matrix, False otherwise Examples -------- >>> from scipy.sparse import coo_array, coo_matrix, csr_matrix, isspmatrix_coo >>> isspmatrix_coo(coo_matrix([[5]])) True >>> isspmatrix_coo(coo_array([[5]])) False >>> isspmatrix_coo(csr_matrix([[5]])) False )rXr)rs&r6isspmatrix_cooro\s. a $$r~c]tRtRtRtRtR#)riwa A sparse array in COOrdinate format. Also known as the 'ijv' or 'triplet' format. This can be instantiated in several ways: coo_array(D) where D is an ndarray coo_array(S) with another sparse array or matrix S (equivalent to S.tocoo()) coo_array(shape, [dtype]) to construct an empty sparse array with shape `shape` dtype is optional, defaulting to dtype='d'. coo_array((data, coords), [shape]) to construct from existing data and index arrays: 1. data[:] the entries of the sparse array, in any order 2. coords[i][:] the axis-i coordinates of the data entries Where ``A[coords] = data``, and coords is a tuple of index arrays. When shape is not specified, it is inferred from the index arrays. Attributes ---------- dtype : dtype Data type of the sparse array shape : tuple of integers Shape of the sparse array ndim : int Number of dimensions of the sparse array nnz size data COO format data array of the sparse array coords COO format tuple of index arrays has_canonical_format : bool Whether the matrix has sorted coordinates and no duplicates format T Notes ----- Sparse arrays can be used in arithmetic operations: they support addition, subtraction, multiplication, division, and matrix power. Advantages of the COO format - facilitates fast conversion among sparse formats - permits duplicate entries (see example) - very fast conversion to and from CSR/CSC formats Disadvantages of the COO format - does not directly support: + arithmetic operations + slicing Intended Usage - COO is a fast format for constructing sparse arrays - Once a COO array has been constructed, convert to CSR or CSC format for fast arithmetic and matrix vector operations - By default when converting to CSR or CSC format, duplicate (i,j) entries will be summed together. This facilitates efficient construction of finite element matrices and the like. (see example) Canonical format - Entries and coordinates sorted by row, then column. - There are no duplicate entries (i.e. duplicate (i,j) locations) - Data arrays MAY have explicit zeros. Examples -------- >>> # Constructing an empty sparse array >>> import numpy as np >>> from scipy.sparse import coo_array >>> coo_array((3, 4), dtype=np.int8).toarray() array([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], dtype=int8) >>> # Constructing a sparse array using ijv format >>> row = np.array([0, 3, 1, 0]) >>> col = np.array([0, 3, 1, 2]) >>> data = np.array([4, 5, 7, 9]) >>> coo_array((data, (row, col)), shape=(4, 4)).toarray() array([[4, 0, 9, 0], [0, 7, 0, 0], [0, 0, 0, 0], [0, 0, 0, 5]]) >>> # Constructing a sparse array with duplicate coordinates >>> row = np.array([0, 0, 1, 3, 1, 0, 0]) >>> col = np.array([0, 2, 1, 3, 1, 0, 0]) >>> data = np.array([1, 1, 1, 1, 1, 1, 1]) >>> coo = coo_array((data, (row, col)), shape=(4, 4)) >>> # Duplicate coordinates are maintained until implicitly or explicitly summed >>> np.max(coo.data) 1 >>> coo.toarray() array([[3, 0, 1, 0], [0, 2, 0, 0], [0, 0, 0, 0], [0, 0, 0, 1]]) rN)r0r1r2r3r7r8rr~r6rrwskr~c6a]tRtRtoRtRtRtRtRtVt R#)ria A sparse matrix in COOrdinate format. Also known as the 'ijv' or 'triplet' format. This can be instantiated in several ways: coo_matrix(D) where D is a 2-D ndarray coo_matrix(S) with another sparse array or matrix S (equivalent to S.tocoo()) coo_matrix((M, N), [dtype]) to construct an empty matrix with shape (M, N) dtype is optional, defaulting to dtype='d'. coo_matrix((data, (i, j)), [shape=(M, N)]) to construct from three arrays: 1. data[:] the entries of the matrix, in any order 2. i[:] the row indices of the matrix entries 3. j[:] the column indices of the matrix entries Where ``A[i[k], j[k]] = data[k]``. When shape is not specified, it is inferred from the index arrays Attributes ---------- dtype : dtype Data type of the matrix shape : 2-tuple Shape of the matrix ndim : int Number of dimensions (this is always 2) nnz size data COO format data array of the matrix row COO format row index array of the matrix col COO format column index array of the matrix has_canonical_format : bool Whether the matrix has sorted indices and no duplicates format T Notes ----- Sparse matrices can be used in arithmetic operations: they support addition, subtraction, multiplication, division, and matrix power. Advantages of the COO format - facilitates fast conversion among sparse formats - permits duplicate entries (see example) - very fast conversion to and from CSR/CSC formats Disadvantages of the COO format - does not directly support: + arithmetic operations + slicing Intended Usage - COO is a fast format for constructing sparse matrices - Once a COO matrix has been constructed, convert to CSR or CSC format for fast arithmetic and matrix vector operations - By default when converting to CSR or CSC format, duplicate (i,j) entries will be summed together. This facilitates efficient construction of finite element matrices and the like. (see example) Canonical format - Entries and coordinates sorted by row, then column. - There are no duplicate entries (i.e. duplicate (i,j) locations) - Data arrays MAY have explicit zeros. Examples -------- >>> # Constructing an empty matrix >>> import numpy as np >>> from scipy.sparse import coo_matrix >>> coo_matrix((3, 4), dtype=np.int8).toarray() array([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], dtype=int8) >>> # Constructing a matrix using ijv format >>> row = np.array([0, 3, 1, 0]) >>> col = np.array([0, 3, 1, 2]) >>> data = np.array([4, 5, 7, 9]) >>> coo_matrix((data, (row, col)), shape=(4, 4)).toarray() array([[4, 0, 9, 0], [0, 7, 0, 0], [0, 0, 0, 0], [0, 0, 0, 5]]) >>> # Constructing a matrix with duplicate coordinates >>> row = np.array([0, 0, 1, 3, 1, 0, 0]) >>> col = np.array([0, 2, 1, 3, 1, 0, 0]) >>> data = np.array([1, 1, 1, 1, 1, 1, 1]) >>> coo = coo_matrix((data, (row, col)), shape=(4, 4)) >>> # Duplicate coordinates are maintained until implicitly or explicitly summed >>> np.max(coo.data) 1 >>> coo.toarray() array([[3, 0, 1, 0], [0, 2, 0, 0], [0, 0, 0, 0], [0, 0, 0, 1]]) cRV9d&VPR4VPR43VR&VPPV4R#)r_r|ryN)rh__dict__update)rmstates&&r6 __setstate__coo_matrix.__setstate__Ws> 5  %yy/51ABE(O U#r~c\R4h)z('coo_matrix' object is not subscriptablerb)rmrJs&&r6r`coo_matrix.__getitem__^sBCCr~c\R4h)z4'coo_matrix' object does not support item assignmentry)rmrJrs&&&r6rzcoo_matrix.__setitem__asNOOr~rN) r0r1r2r3r7rvr`rzr8r9r:s@r6rrs#n`$DPPr~)rrro)r)8r7 __docformat____all__rwarningsrnumpyr1 _lib._utilr_matrixr _sparsetoolsrrr r r r r _baserrrr_datarr_sputilsrrrrrrrrrrrr_indexr r!rDr#rrrirjrrrrorrrr~r6rs8% 7 '000GF.++++9K+ mK+\,F2=2.b .#,<*%6l 7l^|P9|Pr~