ftj%rdZdZgdZddlZddlmZddlZddlm Z dd l m Z dd l m Z mZmZmZmZmZmZdd lmZmZmZmZdd lmZmZdd lmZmZmZm Z m!Z!m"Z"m#Z#m$Z$m%Z%m&Z&m'Z'm(Z(ddl)m*Z*m+Z+ddl,Z,GddeeZ-dZ.dZ/dZ0dZ1dZ2dZ3ddZ4dZ5Gdde-eZ6Gdde e-Z7dS)z2 A sparse matrix in COOrdinate or 'triplet' formatzrestructuredtext en) coo_array coo_matrixisspmatrix_cooN)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_arraysceZdZdZeddZd8dddZedZej d Zed Z e j d Z d Z e j j e _ d9d Ze jj e_ d9dZe jj e_ dZd:dZe jj e_ d;dZe jj e_ ddZejj e_ dZd?dZdZd Zd!Z d;d"Z!d#Z"d$Z#d%Z$d&Z%d'Z&d(Z'd)Z(d*Z)d+Z*d,Z+d-Z,d.Z-d@d0Z.d1Z/d2Z0d3Z1d=d4Z2d5Z3d6Z4d7Z5dS)A _coo_basecoor ANFmaxprintct  tj|||stt|trt ||jrt||j|_| t|j t|t}t fdtt|jD|_t!jg||_d|_n] |\}}n)#t(t*f$r} t)d| d} ~ wwxYw|At-d |Drt+d t d |D}t||j|_| |t|jd  t  fd |D|_t1|||_d|_ndt3|r|j|jkrr~t d|jD|_|jt|||_t|j|j|_|j|_n|} t | j|_| jt|| d|_t| j|j|_d|_n=t!j|} t|t<s7t!j| } | j dkrt)d| j dt| j|j|_|;t||j|jkrd|d|j} t+| | t|j| !}t fd|D|_t1| |||_d|_t|jdkr#t d|jD|_|"dS)Nr*allow_ndmaxval)defaultc3DK|]}tjgVdSdtypeNnparray).0_ idx_dtypes O/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/scipy/sparse/_coo.py z%_coo_base.__init__..*sN$G$G)*%'HRy$A$A$A$G$G$G$G$G$Gr4Tzinvalid input formatc3<K|]}t|dkVdS)rNlenr9idxs r<r=z%_coo_base.__init__..5s,;;S3s88q=;;;;;;r>z4cannot infer dimensions from zero sized index arraysc3hK|]-}tjtj|dzV.dSr N)operatorindexr7maxrBs r<r=z%_coo_base.__init__..8sM"5"5&)#+."="="A"5"5"5"5"5"5r>)r0check_contentsc3FK|]}tj|VdS)copyr5Nr6)r9rCrLr;s r<r=z%_coo_base.__init__..>sH$8$8),%'HSt9$M$M$M$8$8$8$8$8$8r>rKFc3>K|]}|VdSNrLrBs r<r=z%_coo_base.__init__..Es*'J'Js 'J'J'J'J'J'Jr>rOrz!expected 2D array or matrix, not Dzinconsistent shapes: z != c3FK|]}|dVdSFrON)astype)r9rC index_dtypes r<r=z%_coo_base.__init__.._sG$8$8),%(JJ{J$G$G$8$8$8$8$8$8r>c3XK|]%}|tjdV&dSrR)rSr7int64rBs r<r=z%_coo_base.__init__..es4XXS 28% @ @XXXXXXr>)#r__init__r isinstancetupler _allow_ndr_shape_get_index_dtyperHrfloatrangerAcoordsr7r8datahas_canonical_format TypeError ValueErroranyshaperrformatrStocooasarrayr atleast_2dndimnonzero_check)selfarg1rer5rLr+ data_dtypeobjr_er(Mmessager;rTs ` @@r<rWz_coo_base.__init__ sdD8<<<< "!D dE " "= 1tdn555 2)$HHH  11T[9I9I1JJ %eU;;; #$G$G$G$G.3C 4D4D.E.E$G$G$GGG HRz::: ,0))C"&KC!:.CCC#$:;;BC=;;F;;;;;?(*>???!"5"5-3"5"5"555E)%$.III  11&9"R"R"RDK040ID--**$*//C"' "3"3DK #0D0D5 Q QDI"-ci$."Q"Q"QDK05D--Jt$$!$00W a((Av{{'(UAF(U(U(UVVV)!'DNKKK $"54>BBBdkQQ"R%"R"RT["R"R(111"333t{;K;K3LL #$8$8$8$806$8$8$888 #AfIDFFF ,0) t{  a  XXDKXXXXXDK s DD9$D44D9c|jdkr |jdStj|j}|d|S)Nr F)write)rjr_r7 zeros_likecolsetflags)rmresults r<rowz _coo_base.rowisC 9q==;r? "tx((e$$$ r>c|jdkrtdtj||jdj}|jdd|fz|jddz|_dS)Nrz8cannot set row attribute of a 1-dimensional sparse arrayrur4)rjrcr7rhr_r5)rmnew_rows r<r{z _coo_base.rowrsf 9q==WXX X*WDKO,ABBBk#2#&'3dk"##6FF r>c|jdS)Nr}r_)rms r<rxz _coo_base.colys{2r>ctj||jdj}|jdd|fz|_dS)Nr}r4)r7rhr_r5)rmnew_cols r<rxz _coo_base.col}s<*WDKO,ABBBk#2#&'3 r>c t||j|j}t|\}}||jkr|r|S|St |j|j|}t|dkr=|dkrt||d}n7t||dddd}ntj |||}| |jt| t fd |D}|r|j}n|j}|||f|d S) Nr-orderrCr rr}r/c3DK|]}tj|VdSr3r7rh)r9cor;s r<r=z$_coo_base.reshape..s2PPr2:b :::PPPPPPr>FrerL)rrerZr rL _ravel_coordsr_rAdivmodr7 unravel_indexr\rHrYr` __class__) rmargskwargsrerrL flat_coords new_coordsnew_datar;s @r<reshapez_coo_base.reshapesWD$*t~FFF*622 t DJ   yy{{" $DK5III u::??||#Kq:: #Kq::44R4@ )+uEJJJJ))$+c%jj)II PPPPZPPPPP   !y~~''HHyH~~x4E~NNNr>c||dkr|jdkrt|jtfd|jDrt d|jjdkstd|jDrt dt S|dkr ||jz }||jkrt dtjt|jd|z |j d|z S) Nrr c3>K|]}t|kVdSrNr@)r9rCnnzs r<r=z$_coo_base._getnnz..s-::s3s88s?::::::r>z3all index and data arrays must have the same lengthc3,K|]}|jdkVdSrE)rjrBs r<r=z$_coo_base._getnnz..s()O)OC#(a-)O)O)O)O)O)Or>z'coordinates and data arrays must be 1-Daxis out of bounds minlength) rjrAr`rdr_rcintr7bincountrre)rmaxisrs @r<_getnnzz_coo_base._getnnzs cd||tj|jS|dkr ||jz }|dks ||jkrt d|jdk}|jd|z |}tjt||j d|z S)Nrrr r) sum_duplicatesr7 count_nonzeror`rjrcr_rrre)rmrmaskcoords r<rz_coo_base.count_nonzeros  <#DI.. . !88 DI D !88tty((122 2yA~ AH%d+{.u55APTHAUVVVVr>c |jt|jkr,tdt|jd|jt |jD]7\}}|jjdkr"td|d|jjdd8| |jt|j  tfd |jD|_t|j|_|jd krt |jD]\}}| |j |kr5td |d | d|j ||d kr'td|d|dSdS)z' Checks data structure for consistency z2mismatching number of index arrays for shape; got z , expected iz index array z has non-integer dtype () stacklevelr/c3DK|]}tj|VdSr3r)r9rCr;s r<r=z#_coo_base._check..sF55!$Js)<<<555555r>rzaxis z index z exceeds matrix dimension znegative axis z index: N)rjrAr_rc enumerater5kindrnamer\rHrerYrr`rmin)rmrrCr;s @r<rlz_coo_base._checks 9DK(( ( (M$' $4$4MMAEMMNN N  ,, # #FAsy~$$PAPPsy~PPP !####))$+c$*oo)NN 5555(, 55555 di(( 8a<<#DK00 N N37799 1 --$&IQ&I&Iswwyy&I&I9=A&I&IJJJ7799q==$%La%L%L%L%LMMM! < N Nr>c|tjddd}nttrlt |drt |jkrt dt t|jkrt dn|dkrt dtfd|D}tfd|D} j |f|| S) Nr}__len__z"axes don't match matrix dimensionszrepeated axis in transpose)r rzoSparse matrices do not support an 'axes' parameter because swapping dimensions is the only logical permutation.c32K|]}j|VdSrN)r[r9rrms r<r=z&_coo_base.transpose..s)<c32K|]}j|VdSrNrrs r<r=z&_coo_base.transpose..s)==1 A======r>r) r^rjrXrhasattrrArcsetrYrr`)rmaxesrLpermuted_shapepermuted_coordss` r< transposez_coo_base.transposes& <##DDbD)DD g & & :4++ Gs4yyDI/E/E !EFFF3t99~~** !=>>>+ V^^9:: :<<<returnct||j}|jdkrtdt |dkrtdt ||jkrmt |j|j}tj |}tj |d|||_|j d||_ ||_ dSt ||jkr|j dt |dz dzd|jt |z zz}||}|jdt ||_|jdt ||_ tdt!|j|D}|rtjd t!|j|Ds7t)fd |jD|_|j |_ ||_ dS) Nr-rzonly 1-D or 2-D input acceptedz!shape argument must be 1-D or 2-Dr )r}r c3(K|] \}}||kVdSrN)r9oldnews r<r=z#_coo_base.resize..s*MM(#sC#IMMMMMMr>c g|] \}}||k Srr)r9rCsizes r< z$_coo_base.resize..s-***(sDd ***r>c3(K|] }|V dSrNrr9rCrs r<r=z#_coo_base.resize..s'#E#E#CI#E#E#E#E#E#Er>)rrZrjrcrArr_remathprodr7rr`r[rrdzip logical_andreduceallrY)rmrermax_size tmp_shapetmp is_truncatingrs @r<resizez_coo_base.resizesEDN;;; 9q===>> > u::>>@AA A u:: ! !' TZ@@Ky''H*;yy+A5IIDK )8),DIDK F u:: ! ! OSZZ!^O,$)c%jj012  ,,y))C*[c%jj[1DK)KSZZK0DKMMc$*e6L6LMMMMM  ,>((**,/ U,C,C***D88:: ,##E#E#E#E#E#E#EEE  IdO  r>c |||}t|jj}|s|jjst d|jdkrWttj dg|j |j|j d|j | d|n |jdkrH|j\}}t|||j |j|j|j | d|n|r5tjdtj|jdd}nFtjtj|jddddddddd}tj|j }t||j |j||j | d|||jS)Nz&Output array must be C or F contiguousr rArr})_process_toarray_argsrflags f_contiguous c_contiguousrcrjr r7r8rr_r`ravelrer r{rxappendcumprod concatenater) rmroutBfortranrrNstridesr_s r<toarrayz_coo_base.toarray!s  & &uc 2 2ag*++ Gqw3 GEFF F 9>> 28QC==$(DI;q>49aggcllG M M M M Y!^^:DAq 1dh$(DI g / / / / O)Arz$*SbS/'B'BCC)BJtz!""~ddd/C$D$DTTrT$JANN^DK00F 7DHdi!49aggcllG E E Eyy$$$r>cX|jdkrtd|jd|jdkr!||j|jSddlm}||j \}}}}||||f|}|j s| |S) aQConvert 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]]) rz+Cannot convert. CSC format must be 2D. Got rPrr4r ) csc_arrayre) rjrcr_csc_containerrer5_cscr_coo_to_compressed_swaprar)rmrLrindptrindicesr`rexs r<tocscz_coo_base.tocsc<s( 9>>W49WWWXX X 8q==&&tz&DD D ' ' ' ' ' '+/+B+B9?+S+S (FGT5##T7F$;5#IIA, #  """Hr>cj|jdkrtd|jd|jdkr!||j|jSddlm}||j |}|\}}}}||||f|j }|j s| |S) aNConvert this array/matrix to Compressed Sparse Row format Duplicate entries will be summed together. Examples -------- >>> from numpy import array >>> from scipy.sparse import coo_array >>> row = array([0, 0, 1, 3, 1, 0, 0]) >>> col = array([0, 2, 1, 3, 1, 0, 0]) >>> data = array([1, 1, 1, 1, 1, 1, 1]) >>> A = coo_array((data, (row, col)), shape=(4, 4)).tocsr() >>> A.toarray() array([[3, 0, 1, 0], [0, 2, 0, 0], [0, 0, 0, 0], [0, 0, 0, 1]]) rz*Cannot convert. CSR must be 1D or 2D. Got rPrr4r ) csr_arrayrOr) rjrcr_csr_containerrer5_csrrrrrar) rmrLrarraysrrr`rers r<tocsrz_coo_base.tocsr]s( 9q==V$)VVVWW W 8q==&&tz&DD D ' ' ' ' ' ',,Y_4,HHF+1 (FGT5##T7F$;4:#NNA, #  """Hr>c ,||j\}}||jt|j|}|jdkr|r|jdn |jd}t|}tj d|g|}|r|j n|j } ||| |j fS||j\} } t| }| |d} | |d} tj |dz|}tj| |}tj|j |j} t!|||| | |j ||| ||| |j fS)z?convert (shape, coords, data) to (indptr, indices, data, shape)r/r rr4FrO) _shape_as_2dr\r_rHrrjrLrAr7r8r`rerSempty empty_liker5r ) rmswaprLrrrr;rrrr`majorminors r<rz_coo_base._coo_to_compressedstD%&&1))$+c$(A>N>N)OO 9>>/3Gdk!n))+++QGg,,CXq#hi888F'+:49>>###D7D$*4 4tDK(( u%jj YU 33 YU 33!a%y111-Y777}TYdj999!QUE49fgtLLLwdj00r>c2|r|S|SrNrO)rmrLs r<rgz_coo_base.tocoos  99;; Kr>ct|jdkrtd|jd||j|jz }t j|d\}}t|dkr(tdt|dtd |j j d krt j d |j }nUt j t||jd zf|j }|j |||jf<|||f|jS)Nrz+Cannot convert. DIA format must be 2D. Got rPT)return_inversedzConstructing a DIA matrix with z diagonals is inefficientrr)rrr4r r)rjrcrrxr{r7uniquerArrr`rzerosr5rH_dia_containerre)rmrLksdiagsdiag_idxr`s r<todiaz_coo_base.todias5 9>>W49WWWXX X  X )Bt<<<x u::   "3u::"""(Q 8 8 8 8 9>Q  8F$*555DD8SZZ)9:$*MMMD'+yD48# $""D%= "CCCr>cR|jdkrtd|jd|||j|j}|jdkr|jd}nt|j}tt||j |_ |S)Nrz*Cannot convert. DOK must be 1D or 2D. Got rPr4r r) rjrcr_dok_containerrer5r_rdictr`_dict)rmrLdokr_s r<todokz_coo_base.todoks 9q==V$)VVVWW W !!$*DJ!?? 9>>[^FF$+&FVTY//00  r>rc d |jdkrtd|j\}}|| ks||kr tjd|jjStjt|t|dz|t|dz |j}|j |z|j k |j r|j }|j }nGt fd|jD}|||j \\}}}|||t|dz<|S)Nrz diagonal requires two dimensionsrr4c3(K|] }|V dSrNr)r9rC diag_masks r<r=z%_coo_base.diagonal..s'??CY??????r>)rjrcrer7rr`r5rrrHr{rxrarYr__sum_duplicates) rmkrowscolsdiagr{r`indsr:r s @r<diagonalz_coo_base.diagonals0 9>>?@@ @Z d ::d8ATY_555 5xD3q!99,dSAYY.>??"j***X\dh.  $ N(9%C9Y'DD????4;?????D!11$ )8LMMNHS!d $S3q!99_ r>c2|jdkrtd|j\}}|jrt|sdS|jj}|j|jz |k}|dkrt||z|}|jrt|t|}tj ||j|k}tj | | |z|} tj ||} nt|||z }|jrt|t|}tj ||j|k}tj ||} tj |||z|} |jr |d|} n"tj ||j} || dd<tj |j|| ftj |j|| ff|_ tj |j|| f|_d|_dS)Nrz*setting a diagonal requires two dimensionsrr4F)rjrcrerAr{r5rxrr7 logical_orarangerrr_r`ra) rmvaluesrrrrr; full_keep max_indexkeepr~rrs r<_setdiagz_coo_base._setdiags 9>>IJJ Jz1 ; s6{{  FHN Htx'1, q55AaC I{ 8 3v;;77 =DH ,ABBDiQBN)DDDGi ;;;GGAqs I{ 8 3v;;77 =DH ,ABBDi ;;;Gi1y= BBBG ; !jyj)HHx <<?? $)!!!r>Tc|rtd|jD}n|j}|||f|j|jS)zReturns a matrix with the same sparsity structure as self, but with different data. By default the index arrays are copied. c3>K|]}|VdSrNrOrBs r<r=z'_coo_base._with_data.. s*==#388::======r>rer5)rYr_rrer5)rmr`rLr_s r< _with_dataz_coo_base._with_datasS  !=======FF[F~~tVnDJdj~QQQr>ct||j|j\}}}}tjt |jtjg}g}g}tt||j D]1\} \} } t| tr | | kz(t| tr| tdkr|| f| |j| \} } }|dkrI|dkr| | k| | kz}n | | k| | kz}tj| | z |\}}|dk|zzn| | k| | kz}| | z }|z|||| || 3|dkr9|j|jdSfd|D}|j}|r.t)|}|dj}tj|t |dd}tjfd |Ddddddf}||kd }|\}|}fd |D}t3tj|| }t ||d|dz dzkr!|d}|d||z||dz}n||z}|r|rtj|d}n3tjt ||j dj}||d||ddD]*} || |+t?||f||j S)Nr4r rrFrOc g|] }| Srrr9r index_masks r<rz)_coo_base.__getitem__..5s<<<bn<<r}c g|] }| Srrr#s r<rz)_coo_base.__getitem__..K"G"G"Gb2j>"G"G"Gr>rc g|] }| Srr)r9rarr_cos r<rz)_coo_base.__getitem__..Os:::"V*:::r>rr) r$rerfr7onesrAr`bool_rrr_rXrslicerrrsumrSr5r%r8rrrklistrrwrinsertrLr)rmkeyrG new_shape arr_int_posnone_pos slice_coords arr_coords arr_indicesrrCrstartstopstepin_rangenew_ixmrr arr_shapekeyarrfoundarr_ixnew_arr_coordspos coord_liker)r$s @@r< __getitem__z_coo_base.__getitem__s*2C T[3 3 /y+xWS^^28<<<    %c%&=&=>> ( (LAyR#s## (rSy) C'' (%++%% ''++++(+ DJqM(B(B%E4qyy!88(*e T 'BHH(*e T 'BH$&Ib5j$$?$? "qAv&99 $&%KBI#>!#e"h.  ''////!!"%%%""3'''' ??9Z(,,..55dju5MM M<<<<|<<< 9Z(  9+[9K#A,IXk**223{3C3CQKKF"G"G"G"GJ"G"G"GHHAAAtTJz)..A.66E"]]__NFF'H::::z:::J!""26"K"K"KLLN;;r?[^#Ca#GGG!!n'->CDDAQQ ,j8  8 Q]:a=99 Xc(mm4;q>;OPPP   hqk: 6 6 6abb\ 8 8!!!Z__%6%67777(J/y SSSSr>c t||j|j\}}}|rAt|}|dddD]}||t |}|rTtfd|D}t |}t||D] \} | < t|r%d|jvrdSt|||j \} } n;tj ||j }|jdkrdSt||\} } |\} } t#| dkr*t#| ddkr| | c|_|_dSd}d}|rD]}t)|t*srt-|sc|j}t#||d|dz dzkr |d}nd}| ||t#|z}t/||}nd}i}t1D]<\} }| |kr|t#|z }t)|t*r ||| <|dz }=dg|jz}t#| }t1D]\} }t-|rtj||f|| <.t)|t*r<||j| \}}}|| || |zz|| <|||| <| }tj|}tj|dd\}}tj | ||g|_t dt| |dd|fD|_d |_!dS) Nr}cDi|]}t|x|Sr)r")r9rarrrGs r< z)_coo_base.__setitem__..vs1UUU!)5QR8OC:T:TUq#UUUr>rr4r T)r return_indexc3>K|]}tj|VdSrN)r7hstack)r9cs r<r=z(_coo_base.__setitem__..s*VVQBIaLLVVVVVVr>F)"r$rerfr.poprYr%rrr_get_sparse_data_and_coordsr5r7rhr_get_dense_data_and_coords _zero_manyrAr`r_rXr,r"rrrj broadcast_torrrLr8rrKra)rmr0rr1r2r3jarr_posr6rx_datax_coordsold_data old_coordsr=rBrC x_arr_coox_arr_coo_ravelx_axx_axesrnew_nnzr7r8r9rr:indrGrGs @@r< __setitem__z_coo_base.__setitem__es!2C T[3 3 /y+x  )YIddd^ ! ! a    i((I  KKEUUUUU{UUUG+W^^-=-=>Kg{33  3a A;; HAG||:1iTT FHH 1DJ///Av{{9!YGG FH $u55* x==A  #hqk"2"2a"7"7%-z "DIt{ F      !#u--inn # I ;''KOk!n,Lq,PQQ)!n!)S3y>>-A)A BI&3Iy&I&IOE&&  FAsCxxI&#u%%  q  Vdi' f++&& = =FAs~~ =!#wj!A!A 1 C'' =$'KK 1 $>$>!tT!&&))ctjt|jtjg}g}t t ||jD]&\}\}}t|tr ||kz(t|tr|tdkr| |j |\}}} | dkrF| dkr||k||kz} n ||k||kz} tj ||z | } | dk| zz||k||kz} | zt|tr|tdkr||||(|rtj|t|dd} tjfd|Ddddddf}| |kd} | \}}tj}d|d|<|zfd|jD}|j}||fS) Nr4r rr}c g|] }| Srrr#s r<rz(_coo_base._zero_many..r&r>r'Tc"g|] }| Srrr#s r<rz(_coo_base._zero_many..s???RZK???r>)r7r*rAr`r+rrr_rXrr,rremodrr8rrrkrw)rmrGr5r6rrCrr7r8r9r:r<r>r?arr_coor:arr_index_mask pruned_coords pruned_datar$s @r<rPz_coo_base._zero_manys73ty>>:::   %c%&=&=>> ( (LAyR#s## (rSy) C'' (C5;;,>,>$'KK 1 $>$>!tT199axx$&%KBI#>$&%KBI#>rEz400A16X"55JJ "e T :H(*JJC'' (C5;;,>,>!!"%%%""3''''  )Xk**223{3C3CQKKF"G"G"G"GJ"G"G"GHHAAAtTJz)..A.66EJGQ]:66N?CN:--//27; < . (J@???4;??? i , M))r>c|jrdS||j|j}|\|_|_d|_dS)zeEliminate duplicate entries by adding them together This is an *in place* operation NT)rarr_r`)rmsummeds r<rz_coo_base.sum_duplicatessI  $  F%%dk49==!' 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" 5 5rx 7M7M "t{ ; ; $ 5;;s+;+;V E E EM 9>> JqM5;q>:L(C(CC Y!^^!KN,L+a.C-$$C,l;;;5;r?CEKK$4$4f > > >{{U {,,,r>ct|st|s~t|sotj|}|jdkr5|jtjkr tdt|d |j n#t$r|}YnwxYwt|r||zS|j d|j dddkr td|j d|j d |jd kr|jd kr||zSt|r| |S||S) aReturn 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) rz"dot argument not supported type: ''r}ruNzshapes rwz are not aligned for n-D dotr)rr#r!r7rrjr5rrbrrerrc _dense_dot _sparse_dotrg)rmrzo_arrays r<dotz _coo_base.dotsl 75>> \%5H5H mE**G|q  W]bj%@%@ ST%[[ S S STTT  !        %<  :b>U[-a0 0 0> *??5)) ) ...sB BBct||jdz g\}}t|td|jdz g\}}||jz}|}||z}g}|r||fn|f} t |j| D]-\} } |tj | | .t|j |f|S)Nr rrr) _convert_to_2drjrHTrgrr_extendr7rrr`) rmrzself_2d s_new_shapeother_2d o_new_shapercombined_shaper_ new_shapesrLss r<rz_coo_base._sparse_dotes .dTY]ODD .us1ej1n7M7M6N O O+#zz||%{23>Rk;//[N  Z00 2 2DAq MM"*1a00 1 1 1 1$)V,NCCCCr>c|j}|dkr|dkrdn |jdf}|}nt||jdz g\}}|j}|dkr|dkrdn |jdf}|}n|jdd|jddz}|dz gt|dz |dz R}t j||} | |jdtj|f}||z} ||z} | | S)Nrr rrr}ru) rjrerr^r7rrrr) rmrzrrrrrr reorder_dimso_reorgrrs r<rz_coo_base._dense_dotzs* Q;; &! ""$*Q-1AKGG#1$Q#H#H G[ Q;; &! ""%+b/1CKHH+crc*U[-==K"QJGvz):):GFQJGGLl5,77G B;9O9O'PQQH!%{2||N+++r>rcHts~tsotj}|jdkr5|jtjkr tdtd j n#t$r|YnwxYwtjj|\}}tfdt||Drtdtr||S||S)a 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]]) rz#tensordot arg not supported type: 'rc3VK|]#\}}j|j|kV$dSrNr)r9axbxrzrms r<r=z&_coo_base.tensordot..sL992rz"~R0999999r>z*sizes of the corresponding axes must match)r#rr7rrjr5rrbrrer _process_axesrdrrc_dense_tensordot_sparse_tensordot)rmrzr other_array axes_self axes_others`` r< tensordotz_coo_base.tensordotsTru~~ $huoo $-..K1$$):bj)H)H Td5kk T T TUUU $ ! $ $ $# $!.diT J J : 99999 J77999 9 9 KIJJ J 5>> H(( :FF F))%JGG Gs6A>> B  B ct||\}}t||\}}||jz}t|s|S|}||z} g} |r||fn|f} t |j| D]/\} } | r(| tj| | 0t|j | f| S)Nr) rrrrgrr_rr7rrr`)rmrzs_axeso_axesrrrrrrr_rrLrs r<rz_coo_base._sparse_tensordots .dF;; .uf = =+#~~ Kzz||%{23>Rk;//[N  Z00 6 6DAq 6 b.q!44555$)V,NCCCCr>ctj}tj}fdt|D}fdD}fd|D}fdt|D} fdD} fd| D} |z} t j| ddz| ddz} g|t j|R}g| ddt j| | ddR}| || |S)Ncg|]}|v| Srr)r9rrs r<rz._coo_base._dense_tensordot..#BBBA!6//a///r>c*g|]}j|Srrrs r<rz._coo_base._dense_tensordot..$s666! 1 666r>c*g|]}j|Srrrs r<rz._coo_base._dense_tensordot..%s>>>aDJqM>>>r>cg|]}|v| Srr)r9rrs r<rz._coo_base._dense_tensordot..'rr>c*g|]}j|Srrr9rrzs r<rz._coo_base._dense_tensordot..(s7771 A777r>c*g|]}j|Srrrs r<rz._coo_base._dense_tensordot..)s???qEKN???r>r}) rArer^rr7rrrr)rmrzrrrr s_non_axes s_axes_shapes_non_axes_shape o_non_axes o_axes_shapeo_non_axes_shapeleftright reshape_left reshape_rights```` r<rz_coo_base._dense_tensordotsTZU[!!BBBBvBBB 6666v666 >>>>:>>>BBBBvBBB 7777777 ????J???~~j6122 UJssOf$c@|jdkr |jdkrtj||S|j}|j}t j|dd|dd}t ||ddz}t ||ddz}||}||}t|} t|} | | z } t| g||jd|jdRS)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. rNrur}r) rjrrrer7rrYr _block_diagrg_extract_block_diag) rmrzrrrself_new_shapeother_new_shapeself_broadcastedother_broadcastedself_block_diagother_block_diagprod_block_diags r<rz_coo_base._matmul_sparse4s3 9q==UZ!^^)$66 6Z k -j"o{3B3?OPP//*RSS/A00;rss3CC--n==!//@@&&677&'899+-==DDFF# EOETZ^EU[_EE    r>c|j|kr|r|n|S|j}t|t|krtddt|t|z zt |z}t dt ||Drtd|d||}t|j t|}|j }|j }|dd}d} |d|dkrT|d} | | z} tj || }tjtjd | | |j} | f}t#d t|dz dD]} || || kr|| } | | z} t|} tj || }t tj || dzd| }tjtjd | | | } | f|z}tj || | } | f|z}t%||f|S) NzDNew shape must have at least as many dimensions as the current shaperc34K|]\}}|dko||kVdSrEr)r9ons r<r=z*_coo_base._broadcast_to..ms3EEtq!Q!16EEEEEEr>zcurrent shape z- cannot be broadcast to new shape {new_shape}r/r}r rr4ru)rerLrArcrYrdrrrr_rHr`r7tilerepeatrrr^r)rmr1rL old_shaperer;r_rr cum_repeat repeat_countnew_dimrrs r<rz_coo_base._broadcast_to^s : " ""&0499;;;D 0J  y>>C NN * *566 6IY785;K;KK EEs5)/D/DEEE E E CBiBBBCC C||E""#DKIGGG 9BCC[  9 " % %$R=L , &Jwx66Hi !\ K K KTXVVG!JrSZZ\?B// 5 5AQx9Q<''(| l* (mm78\::"27:acdd+;\#J#JKK )BIaY$O$O$OQTUU%Z*4 '&)Z88%Z*4 (J/;;;r>ct||\}}tj|jddf|}||z||d<|S)Nr r4.)rr7r*rer)rmr res_dtyperA2dr1r*s r<_sum_ndz_coo_base._sum_ndsT'd33Yw ! a( :::$J'' 22C r>ct||\}}|d||}tj|jd|}t |j|f|S)Nr r)r_min_or_max_axisr7rr_rr`)rmr min_or_maxexplicitrr1resunraveled_coordss r<_min_or_max_axis_ndz_coo_base._min_or_max_axis_nds]'d33Y""1j(;;+CJqM9EE#($45yAAAr>ct||\}}|d|||}||S)Nr )r_argminmax_axisr)rmr argminmaxcomparerrr1res_flats r<_argminmax_axis_ndz_coo_base._argminmax_axis_ndsB'd33Y&&q)WhGG ***r>)NNFrN)NF)rN)NN)Fr)T)r)6__name__ __module__ __qualname___formatr^rZrWpropertyr{setterrxrr__doc__rrrlrrrrrrrgrr rrrr rDr^rPrrrtr{r~rrrrrrrrrrrrrrrrrr>r<r'r's@Ga IGTGGGGGRX ZGGZG X Z44Z4!O!O!OFo-GO;;;;(o-GO W W W W$19MNNN2????$ )1I$$$$L^+FN%%%%2o-GOB    D11116 M)EMDDDD,M)EM    M)EM($,4H$*$*$*NRRRRRTRTRThn*n*n*`)*)*)*V ) ) ) )&>>>3330      B'''2[[[z!-!-!-FN/N/N/`DDD*,,,6nHnHnHnH`DDD4LLL*( ( ( T5<5<5<5r'c|jdkrtdtj|jdd}|jd}|jd}||||f}|jd|jd|jdzz|jd|jd|jdzzf}||z||zf}t|jt|f|S) z Converts an N-D COO array into a 2-D COO array in block diagonal form. Parameters: self (coo_array): An N-Dimensional COO sparse array. Returns: coo_array: A 2-Dimensional COO sparse array in block diagonal form. rzarray must have atleast dim=2Nrur}r rr) rjrcrrrerr_rr`rY)rm num_blocksn_coln_rowres_arrrr1s r<rrs y{{89994:crc?++J JrNE JrNEllJu566GqGN1- a0@@@qGN1- a0@@@J e#Z%%78I diz!2!239 E E EEr>c|d|d}}|j}|j|j}}tjt ||jft}||z|d<||z|d<||z}tt |dz ddD]} || } || z|| <|| z}t|t|f|S)Nrur}r4rr) r`r{rxr7rrArrr^rrY) rmrer r r`r{rxrtemp_block_idxrrs r<rrs9eBi5E 9DxC3u::tx0<<2r * *00Qx&- 1 '4/ dE*--.e < < <cJ|}|t|j}|j|d}|j}||kr||fSt|t|z }|dkrFdg|zt|z}tj |d}t|g|z|z}|dkrct| D]R}|ddkr|dd}|dd}#|ddkr|dd}|dd}Dtdd} tt||D]\} \} } | | kr| dkrtdt|d} tjtj| | || <t|D]$\}}|| kr tj|| ||<%| | z} tj|| }||fS)NFrOrr r}zshape mismatch in assignment)rgrr.r_r`rSrerAr7 zeroslikerYr^rcrrrrrr)rr1r5rUrTx_shapelen_diff coord_zerosr: tot_expandrnnnxx_nnzrRrs r<rNrNs:  AAH~~H V]]5u] - -FgGGx9~~G ,H!||#.4==0l8A;// +1H<==!||y!! A AAqzQ!!""+#ABB<!!!#2#,#CRC= !?@@@J Y!8!899   8B 88  77;<< <HQK  i " u55 x(( * *EArAvv'"b//HQKKb WV\\^^Z 0 0F 8 r>cB|j|kr'tj||}|dkrHt tjdggt |z}|}n|}||}||fS)Nrr) rer7rQsqueezerYr8rArrk)rr1rUrTs r<rOrO sw) OAIIKK 3 3B"(A3--3y>>9::99;;8 8 r>ct|trf|dks|tkrtdt t |z }t t |}nt|t tzrt|dkrtd|\}}t|t|krtdtfd|Dstfd|Drtdntd fd |D}fd |D}||fS) Nr z.axes integer is out of bounds for input arraysrz%axes must be a tuple/list of length 2z,axes lists/tuples must be of the same lengthc32K|]}|kp| kVdSrNr)r9rndim_as r<r=z _process_axes..&2==rV|+rVG|======r>c32K|]}|kp| kVdSrNr)r9rndim_bs r<r=z _process_axes..'rr>z/axes indices are out of bounds for input arraysz3axes must be an integer or a tuple/list of integersc*g|]}|dkr|zn|Srr)r9rrs r<rz!_process_axes..,) E E EdtaxxdVmmT E E Er>c*g|]}|dkr|zn|Srr)r9rr!s r<rz!_process_axes..-r#r>) rXrrrcr.r^rYrArdrb)rr!raxes_aaxes_bs`` r<rrs$O !88tc&&1111MNN NeFTM62233eDkk"" D%$, ' ' O t99>>DEE E v;;#f++ % %KLL L ====f=== = = P ====f=== = = PNOO O PMNNN E E E Ef E E EF E E E Ef E E EF 6>r>c`tfdD}tfdD}t||}tj}tfdt |D}|rstfd|D}tfd|D}t||} | |f} t j|t j|f} n|f} t j|f} d}tj| f| } | |fS)Nc32K|]}j|VdSrNrr9rr(s r<r=z!_convert_to_2d..2s)44! 1 444444r>c32K|]}j|VdSrNrr)s r<r=z!_convert_to_2d..3s)22sy|222222r>c3$K|] }|v|V dSrNr)r9rrs r<r=z!_convert_to_2d..7s'==1q}}Q}}}}==r>c32K|]}j|VdSrNrr)s r<r=z!_convert_to_2d..9s)@@! 1 @@@@@@r>c32K|]}j|VdSrNrr)s r<r=z!_convert_to_2d..:s)>>sy|>>>>>>r>rr) rYrrAr_r^rrrr`) r(r axis_coords axis_shape axis_ravelrjnon_axisnon_axis_coordsnon_axis_shapenon_axis_ravel coords_2dshape_2dnew_coos `` r<rr1sS4444t44444K2222T22222J{J77J sz??D====d =====H @@@@x@@@@@>>>>X>>>>>&GG#Z0 In--ty/D/DEM Ij))+9-X>>>G N ""r>rc&t|dkr|dSt|dkr|\}}|\}}|dkrT|td|dz ztd|dz z}t|}tj||||zS|dkrT|td|dz ztd|dz z}t|}tj||||zSt dtj||| S) z;Like np.ravel_multi_index, but avoids some overflow issues.r rrrr/r4Fz'order' must be 'C' or 'F'r)rArHrr7rrcravel_multi_index) r_rernrowsncolsr{rxr0r;s r<rrGs' 6{{aay 6{{a uS C<<c!UQY///#a2C2CCF'v666I;uc;;;cA A c\\c!UQY///#a2C2CCF'v666I;uc;;;cA A9:: : U ; ; ;;r>c,t|tS)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 r<rr\s. a $ $$r>ceZdZdZdS)ra  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]]) N)rrrr rr>r<rrws"kkkkr>rc$eZdZdZdZdZdZdS)ra. 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]]) cd|vr-|d|df|d<|j|dS)Nr_r{rx)rM__dict__update)rmstates r< __setstate__zcoo_matrix.__setstate__WsQ 5  %yy//51A1ABE(O U#####r>c td)Nz('coo_matrix' object is not subscriptablerb)rmr0s r<rDzcoo_matrix.__getitem__^sBCCCr>c td)Nz4'coo_matrix' object does not support item assignmentrF)rmr0rs r<r^zcoo_matrix.__setitem__asNOOOr>N)rrrr rDrDr^rr>r<rrsVnn`$$$DDDPPPPPr>r)r)8r  __docformat____all__rwarningsrnumpyr7 _lib._utilr_matrixr _sparsetoolsr r r rrrr_baserrrr_datarr_sputilsrrrrrrrrr r!r"r#_indexr$r%rFr'rrrNrOrrrrrrrr>r<rSs88% 7 7 7 ''''''000000000000000000GFFFFFFFFFFF........++++++++++++++++++++++++++++98888888K+K+K+K+K+ mK+K+K+\,FFF2===2...b   .###,<<<<*%%%6lllll 7lll^|P|P|P|P|P9|P|P|P|P|Pr>