(phcNSrSr/SQrSSKrSSKJr SSKJr SS K J r J r J r J r SS KJr SS KJrJrJrJrJrJr SS KJr "S S\5rSrSr"SS\\ 5r"SS\\5rg)zSparse DIAgonal formatzrestructuredtext en) dia_array dia_matrixisspmatrix_diaN)copy_if_needed)spmatrix)issparse_formats_spbasesparray) _data_matrix)isshape upcast_chargetdtype get_sum_dtype validateaxis check_shape) dia_matvecc\rSrSrSrSSS.SjjrSrSrSSjr\ RR\l SS jr \ RR\ l SS jr \ RR\ l S r S rS rSSjrSSjr\ R"R\l SSjr\ R$R\l SSjr\ R&R\l SSjr\ R(R\l SSjr\ R*R\l SSjrSr\ R.R\l Srg) _dia_basediaNmaxprintc[R"XUS9 [U5(aURS:XaUU(aUR 5nUR UlUR Ul[UR5Ul GOwURUR:XaU(aUR 5nOUR5nUR UlUR Ul[UR5Ul GO[U[5(Ga2[U5(av[U5Ul [R"S[!U["S95UlUR%['UR5S9n[R"SUS9UlGO[UupUc [)S5eU(d[*n[R,"[R."USX4S 95Ul[R."US UR%['U5S9US 9n [R0"U 5Ul[U5Ul O[R4"U5n[U[65(a)UR8S:wa[)SUR8S35eUR;XUS9R5nUR UlUR Ul[UR5Ul Ub+[!U5n UR R=U 5UlUR R8S :wa [)S5eUR R8S:wa [)S5eUR RS[?UR 5:wa:[)SUR RS[?UR 54-5e[?[R@"UR 55[?UR 5:wa [)S5eg![2an S n [)U 5U eSn A ff=f![2an [)S URS 35U eSn A ff=f)Nrr)rr)defaultmaxvalrdtypezexpected a shape argument)r!copyrz+unrecognized form for dia_array constructorzunrecognized form for z_matrix constructorrzDIA arrays don't support zD input. Use 2D)r!shapezoffsets array must have rank 1zdata array must have rank 2zBnumber of diagonals (%d) does not match the number of offsets (%d)z&offset array contains duplicate values)!r__init__r formatr"dataoffsetsrr#_shapetodia isinstancetuplernpzerosrfloat_get_index_dtypemax ValueErrorr atleast_2darray atleast_1d Exceptionasarrayr ndim_coo_containerastypelenunique) selfarg1r#r!r"rA idx_dtyper&r'emessagenewdtypes D/var/www/html/venv/lib/python3.13/site-packages/scipy/sparse/_dia.pyr$_dia_base.__init__swd8< D>>{{e#99;D II #|| )$**5 ;;$++-$ A AFF  yy )!''2 e $ $t}}*$/ HHUHUE,JK  11TZZ1I !xx9= 5$(MD }()DEE- " bhhtAwe.W XDI hhtAw-1-B-B#e*-B-U,02G$&==#9DL"-e"4DK Mzz$'$((TYY!^ # > 99>>Q :; ; 99??1 T\\!2 2@yyq)3t||+<=>? ? ryy& '3t||+< <EF F =Y!5KG$W-145$ M !9$(KK=0C"EFKLM Ms0(P0Q0 Q :QQ  Q9Q44Q9c [URup[U[5(aSOSnURR SnSUSUSUR SURSUS UR S 3 $) Nr3matrixr)r r%r*r r&r#r!nnz)r<_fmt sparse_clsds rC__repr___dia_base.__repr__cs}$++& *4 9 9Wx IIOOA uHZL DJJ<@hhZ1!4J4::,VW Y cURup[R"URRS5nX0RSS2S4- nUS:nXTU:-nXSU:-nU$)zvReturns a mask of the same shape as self.data, where mask[i,j] is True when data[i,j] corresponds to a stored element.rNr)r#r,aranger&r')r<num_rowsnum_cols offset_indsrowmasks rC _data_mask_dia_base._data_masklsg"ZZii  23 LL400q x  x'( rPcUb [S5eUR5n[R"URU5$)Nz>$,, 6 6??499uzz#9: :jju}}= ??; =OOK? #**""1%  [!1S5F!Fyyx!89H \ "ejj 0 "  3{#3s5==7I#Izz- ":;H q[ !TYY . !DJJqMKO3TZZ]CAxx[!1%nnTYY ;H ww& '499QU+; ; ' ( )UZZ2A2-> > )""H#:$**"MMrPc>URURU-5$N)rzr&)r<rs rC _mul_scalar_dia_base._mul_scalarstyy5011rPc Un[R"URS[URR URR 5S9nUR RSnURupV[XV[UR5X@RUR UR5UR55 U$)Nrr r) r,r-r#rr!charr&rr:r'ravel)r<rrpyLrbrcs rC_matmul_vector_dia_base._matmul_vectors  HHTZZ]+djjoo78ww||+E F IIOOA jj1DLL)1llDII779aggi )rPcfURup4URS:Xa[RnO [ U5nUS:a[ X2-XE5nSnUnO[ X4U- U5nUnX&-nURS:waUSUnUR RupX R;aiX:aE[R"X4UR RS9n UR U SS2SU 24'XlXR URU:HXx24'g[R"URURRRU55Ul[X5n [R"U S-U 4UR RS9n UR U SS2SU 24'XSXx24'Xlg)Nrr rrv) r#r7r,infr:r`r&r'r-r!appendtyper0) r<valuesrdrbrcvalues_nn min_index max_index data_rows data_colsr&ms rC_setdiag_dia_base._setdiagsuzz ;;! vvH6{H q5AE1'AIIA1uh'AII ;;! BQZF#yy $xx 6diiooN&*iiQ  ]# @FIIdlla')<< =99T\\4<<3E3E3J3J13MNDLI)A88Y]A.diiooFD$(IID"jyj !,2Y(( )IrPc4U(aUR5$U$rr")r<r"s rCr)_dia_base.todias 99; KrPcUbUS:wa [S5eURup4[UR5nUR*n[R "[ U5[RS9SS2S4n[R "U[RS9Xe-SS2S4- n[SXPRRS- 5n [R"UR[R"URRSU 4URRS945n XU4n URX4XC4US9$)N)rrzvSparse arrays/matrices do not support an 'axes' parameter because swapping dimensions is the only logical permutation.r rr)r#r") r1r#r0r'r,rRr:intcr&hstackr-r!r) r<axesr"rSrTmax_dimr'rc pad_amountr&s rC transpose_dia_base.transposes'  LM M"ZZdjj/<<- IIc'l"'' 21d7 ; IIhbgg .'2CQW1M MGIIOOA$667 yy$))RXXtyyq/A:.N48IIOO&EFGqDz""D? ; &*#, ,rPcURup#X*::dX:a)[R"SURRS9$[R "UR U:H5un[SU5n[X!-U5nXe- nURS:Xa([R"XpRRS9$URUSXV24nU[U5- n U S:a[R"USU 4SS9nU$)Nrr constant)mode) r#r,emptyr&r!nonzeror'r0r`sizer-r:pad) r<rdrowscolsidx first_collast_col result_sizeresultpaddings rCdiagonal_dia_base.diagonal1sZZ  :88ATYY__5 5zz$,,!+,1I tx&* 88q=88Kyy? ?3q69#556F + Q;VVFQLzBF rPcURS:Xa$URURURS9$URup#URRupE[ R "U5nX`RSS2S4- nUS:nXU:-nXU:-nXRS:g-nUR[UR5S9n [ R"US-U S9n [ R"URSS9SU5U SUS-&XS:a XXS-S&URURRU SS9n URRURn URXU 4URURS9$) Nrr rrrhFr)r#r!)rI_csc_containerr#r!r&r,rRr'r/r0r-cumsumrkTr9) r<r"rSrT num_offsets offset_lenrUrVrWr?indptrindicesr&s rCtocsc_dia_base.tocscCsp 88q=&&tzz&D D!ZZ"&))// ii + LL400q x  x'( a ))TZZ)A (Q,i8!#4888+;IX+F!GqA  $*$6Fa<= !%%-&&yu&=yy{{466"""D6#:$**)-#5 5rPcNURup#URRupE[R"U5nX`RSS2S4- nUS:nXU:-nXU:-nXRS:g-nXxn[R "Xd5UR 5n URUR4[UR5S9n URU SS9nU RU SS9n URUn URXU 44URURSS9$)Nr)arraysrFr)r#r!r") r#r&r,rRr'tilerr/r0r9r8r!) r<r"rSrTrrrUrVrWcolr?r&s rCtocoo_dia_base.tocoo]s !ZZ"&))// ii + LL400q x  x'( a iggk/ =))LL?3tzz?* jjj/jjj/yy"" : djj #  rPcU(a3URXRR54URS9$URXR4URS9$)zReturns a matrix with the same sparsity structure as self, but with different data. By default the structure arrays are copied. rw)rr'r"r#)r<r&r"s rCrz_dia_base._with_dataxsb &&||((*+4::' &&||$DJJ' rPc[U5nUup#URSS2SU24UlX RS:a[R"UR URS-URRS:5(a`UR SS2S4URS-[R "URRS5:*nSURU'Xlg)Nrr)rr&r#r,anyr'rRr()r<r#rbrcrWs rCresize_dia_base.resizesE"IIa!e$ 1 t||djjm3diiooa6HHIILLD)DJJqM9IIdiiooa012DDIIdO rP)r(r&r')NNFr)NNN)r)F)NF)T)__name__ __module__ __qualname____firstlineno___formatr$rNrXr\r __doc__rerkrxrrrr)rrrrrzr__static_attributes__rjrPrCrrs GKGTKGZ  1$1199M oo--GO!6F++%%CK#NJ2 #J MM))EM,, ))11I ''//H50MM))EM 0MM))EM  ^^++FNrPrct[R"U5n[R"[U55X'U$)z)Helper function to invert an index array.)r, zeros_likerRr:)rinvs rCr}r}s) -- CyyS"CH JrPc"[U[5$)aIs `x` of dia_matrix type? Parameters ---------- x object to check for being a dia matrix Returns ------- bool True if `x` is a dia matrix, False otherwise Examples -------- >>> from scipy.sparse import dia_array, dia_matrix, coo_matrix, isspmatrix_dia >>> isspmatrix_dia(dia_matrix([[5]])) True >>> isspmatrix_dia(dia_array([[5]])) False >>> isspmatrix_dia(coo_matrix([[5]])) False )r*r)rps rCrrs. a $$rPc\rSrSrSrSrg)riaH Sparse array with DIAgonal storage. This can be instantiated in several ways: dia_array(D) where D is a 2-D ndarray dia_array(S) with another sparse array or matrix S (equivalent to S.todia()) dia_array((M, N), [dtype]) to construct an empty array with shape (M, N), dtype is optional, defaulting to dtype='d'. dia_array((data, offsets), shape=(M, N)) where the ``data[k,:]`` stores the diagonal entries for diagonal ``offsets[k]`` (See example below) Attributes ---------- dtype : dtype Data type of the array shape : 2-tuple Shape of the array ndim : int Number of dimensions (this is always 2) nnz size data DIA format data array of the array offsets DIA format offset array of the array T Notes ----- Sparse arrays can be used in arithmetic operations: they support addition, subtraction, multiplication, division, and matrix power. Sparse arrays with DIAgonal storage do not support slicing. Examples -------- >>> import numpy as np >>> from scipy.sparse import dia_array >>> dia_array((3, 4), dtype=np.int8).toarray() array([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], dtype=int8) >>> data = np.array([[1, 2, 3, 4]]).repeat(3, axis=0) >>> offsets = np.array([0, -1, 2]) >>> dia_array((data, offsets), shape=(4, 4)).toarray() array([[1, 0, 3, 0], [1, 2, 0, 4], [0, 2, 3, 0], [0, 0, 3, 4]]) >>> from scipy.sparse import dia_array >>> n = 10 >>> ex = np.ones(n) >>> data = np.array([ex, 2 * ex, ex]) >>> offsets = np.array([-1, 0, 1]) >>> dia_array((data, offsets), shape=(n, n)).toarray() array([[2., 1., 0., ..., 0., 0., 0.], [1., 2., 1., ..., 0., 0., 0.], [0., 1., 2., ..., 0., 0., 0.], ..., [0., 0., 0., ..., 2., 1., 0.], [0., 0., 0., ..., 1., 2., 1.], [0., 0., 0., ..., 0., 1., 2.]]) rjNrrrrrrrjrPrCrrHrPrc\rSrSrSrSrg)ria[ Sparse matrix with DIAgonal storage. This can be instantiated in several ways: dia_matrix(D) where D is a 2-D ndarray dia_matrix(S) with another sparse array or matrix S (equivalent to S.todia()) dia_matrix((M, N), [dtype]) to construct an empty matrix with shape (M, N), dtype is optional, defaulting to dtype='d'. dia_matrix((data, offsets), shape=(M, N)) where the ``data[k,:]`` stores the diagonal entries for diagonal ``offsets[k]`` (See example below) 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 DIA format data array of the matrix offsets DIA format offset array of the matrix T Notes ----- Sparse matrices can be used in arithmetic operations: they support addition, subtraction, multiplication, division, and matrix power. Sparse matrices with DIAgonal storage do not support slicing. Examples -------- >>> import numpy as np >>> from scipy.sparse import dia_matrix >>> dia_matrix((3, 4), dtype=np.int8).toarray() array([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], dtype=int8) >>> data = np.array([[1, 2, 3, 4]]).repeat(3, axis=0) >>> offsets = np.array([0, -1, 2]) >>> dia_matrix((data, offsets), shape=(4, 4)).toarray() array([[1, 0, 3, 0], [1, 2, 0, 4], [0, 2, 3, 0], [0, 0, 3, 4]]) >>> from scipy.sparse import dia_matrix >>> n = 10 >>> ex = np.ones(n) >>> data = np.array([ex, 2 * ex, ex]) >>> offsets = np.array([-1, 0, 1]) >>> dia_matrix((data, offsets), shape=(n, n)).toarray() array([[2., 1., 0., ..., 0., 0., 0.], [1., 2., 1., ..., 0., 0., 0.], [0., 1., 2., ..., 0., 0., 0.], ..., [0., 0., 0., ..., 2., 1., 0.], [0., 0., 0., ..., 1., 2., 1.], [0., 0., 0., ..., 0., 1., 2.]]) rjNrrjrPrCrrrrPr)r __docformat____all__numpyr, _lib._utilr_matrixr _baser r r r _datar_sputilsrrrrrr _sparsetoolsrrr}rrrrjrPrCrsm% 7'77%A, A,H %6I 7IXI9IrP