(phxSrSr/SQrSSKJr SSKrSSKJr SSK J r SS K J r J r SS KJr SS KJrJrJrJr SS KJrJrJrJrJrJr SS KJr SSKJrJrJ r J!r!J"r"J#r#J$r$ "SS\\ 5r%Sr&"SS\%\5r'"SS\ \%5r(g)z"Compressed Block Sparse Row formatzrestructuredtext en) bsr_array bsr_matrixisspmatrix_bsr)warnN)copy_if_needed)spmatrix) _data_matrix _minmax_mixin) _cs_matrix)issparse_formats_spbasesparray)isshapegetdtypegetdata to_nativeupcast check_shape) _sparsetools) bsr_matvec bsr_matvecscsr_matmat_maxnnz bsr_matmat bsr_transposebsr_sort_indices bsr_tocsrc\rSrSrSrS!SS.SjjrS"Sjr\S\4Sj5r S#S jr \ RR\ l S#S jr \ RR\ l S rS$S jr\ RR\l S rSrSrSrSrSrS%SjrS&Sjr\ R.R\l S&Sjr\ R0R\l S"SjrS'Sjr\ R4R\l S%Sjr\ R6R\l SrSrSrSrS'Sjr S"Sjr!S&Sjr"S r#g)( _bsr_basebsrNmaxprintc [R"XUS9 [U5(aURUR:XaU(aUR 5nOUR US9nUR URURUR4uUlUlUlUl GO[U[5(Ga[U5(a[U5Ul URupxUcSnO)[U5(d[SU35e[U5n[ R""SU-[%U[&S95UlUupXy-S:wdX-S:wa [S5eUR)[+Xy-X-X5S 9n [ R""SU S 9Ul[ R""Xy-S -U S 9UlGO[-U5S :XahUR/XUS 9n U R US9n U R U RU RU R4uUlUlUlUl GO[-U5S:XGa<UupnS nUb [+U5nUb[+U[+U55nUR)UU4USS9n U(d[0n[ R2"XU S9Ul[ R2"UXKS9Ul[5XUS9UlURR6S:wa"[SURR35eUbl[U5(d[SU35e[U5URRS S:wa([SUSURRS S35eO[S5e[ R8"U5n[U[<5(a)UR6S :wa[SUR6S35eUR/XS 9R US9nUR URURUR4uUlUlUlUl Ub[U5Ul OgURcZ[-UR 5S - nURR+5S -nUR>up[Xy-X-45Ul URcUc [S5e[U5Ul Ub2URRA[%X0R5SS9UlURCSS9 g![:an[SURS35UeSnAff=f![:an[S5UeSnAff=f)Nr# blocksize)rrzinvalid blocksize=r)defaultrz#shape must be multiple of blocksizemaxvaldtyper)r-shapeT)r+check_contents)copyr-z*BSR data must be 3-dimensional, got shape=zmismatching blocksize=z vs z(unrecognized bsr_array constructor usagezunrecognized form for z_matrix constructorzBSR arrays don't support zD input. Use 2Dz!unable to infer matrix dimensionszneed to infer shapeFr2) full_check)"r __init__r formatr2tobsrindptrindicesdata_shape isinstancetuplerrr/ ValueErrornpzerosrfloat_get_index_dtypemaxlen_coo_containerrarrayrndimasarray Exceptionrr'astype check_format)selfarg1r/r-r2r'r$MNRC idx_dtypecoor"r:r9r8r+es D/var/www/html/venv/lib/python3.13/site-packages/scipy/sparse/_bsr.pyr5_bsr_base.__init__sd8< D>>{{dkk)dyy{zzIz6 T\\499dkkA =DKty$+U # #t}})$/ jj$ %I"9--(+=i[)IJJ %i 0IHHTI%5xu7UV Ea>Q&$DTYY__DUV("9--(+=i[)IJJY'499??12+>>(4YK"499??12#6"79 !!KLL Mzz$'$((TYY!^ #- U99U>cURup#URupEURRRS:wa,[ SURRR S3SS9 URRRS:wa,[ SURRR S3SS9 URRS:wdURRS:wa [S5eURRS :wa [S 5e[UR5X$-S-:wa([S [UR5X$-S-4-5eURS S :wa [S 5e[UR5[UR5:wa [S5eURS[UR5:a [S5eUR5 U(GaJURS :aURR5X5-:a*[SX5-URR54-5eURR5S :a [S5e[ R""UR5R5S :a [S5eUR%URUR45n[ R&"URUS9Ul[ R&"URUS9Ul[)UR5Ul gg)aCheck whether the array/matrix respects the BSR format. Parameters ---------- full_check : bool, optional If `True`, run rigorous check, scanning arrays for valid values. Note that activating those check might copy arrays for casting, modifying indices and index pointers' inplace. If `False`, run basic checks on attributes. O(1) operations. Default is `True`. iz$indptr array has non-integer dtype ()r.) stacklevelz%indices array has non-integer dtype (rz!indices, and indptr should be 1-Dr0zdata should be 3-Dz&index pointer size (%d) should be (%d)rz!index pointer should start with 0z*indices and data should have the same sizezQLast value of index pointer should be less than the size of index and data arraysz-column index values must be < %d (now max %d)z column index values must be >= 0z8index pointer values must form a non-decreasing sequencer,N)r/r'r8r-kindrnamer9rGr>r:rDprunennzrCminr?diffrBrHr)rLr4rNrOrPrQrRs rUrK_bsr_base.check_formatsjjnn ;;   ! !S ( 7 8I8I8N8N7OqQ  <<   " "c ) 89K9K9P9P8QQRS  <<   !T[[%5%5%:@A A 99>>Q 12 2  q (E!$T[[!114!8 <=> > KKNa @A A  TYY /IJ J KKOc$,,/ /DE E xx!|<<##%-$%T()dll.>.>.@'A&BCC<<##%)$%GHH774;;'++-1$&BCC--t||T[[.IJI**T[[ BDK::dll)DDL!$)),DI returnc4URRSS$)zBlock size of the matrix.rN)r:r/)rLs rUr'_bsr_base.blocksizesyyqr""rccvUb [S5eURup#[URS5U-U-$)Nz6_getnnz over an axis is not implemented for BSR formatr[)NotImplementedErrorr'intr8)rLaxisrPrQs rU_getnnz_bsr_base._getnnzsB  %'78 8~~4;;r?#a'!++rccfUb [S5e[R"UR55$)Nz:count_nonzero over axis is not implemented for BSR format.)rhr? count_nonzero _deduped_data)rLrjs rUrn_bsr_base.count_nonzeros4  %L  2 2 455rcc [URup[U[5(aSOSnSR SUR 55nSUSUSUR SURS US URS 3 $) NrFmatrixxc38# UHn[U5v M g7fN)str).0rss rU %_bsr_base.__repr__..s4^SVV^s) rr6r<rjoinr'r-r_r/)rL_fmt sparse_clsbs rU__repr___bsr_base.__repr__s$++& *4 9 9Wx HH4T^^4 4uHZL DJJ<@hhZ;A3l4::,VW Y rcc URup#X*::dX:a)[R"SURRS9$UR upE[R "[U[US5-U[US5- 5[UR5S9n[R"XU-X5-XEURUR[R"UR5U5 U$Nrr,)r/r?emptyr:r-r'r@r`rCrr bsr_diagonalr8r9ravel)rLkrowscolsrPrQys rUdiagonal_bsr_base.diagonalsZZ  :88ATYY__5 5~~ HHSAq )4#a)+;<!$**- /!!!QY 1"&++t||"$((499"5q :rcc[erurh)rLkeys rU __getitem___bsr_base.__getitem__!!rcc[erur)rLrvals rU __setitem___bsr_base.__setitem__rrcc>URSS9RU5$NFr3)tocoo _add_dense)rLothers rUr_bsr_base._add_dense szzuz%0077rcc @URup#URupE[R"URS[ UR UR 5S9n[ X$-X5-XEURURURR5X5 U$r) r/r'r?r@rr-rr8r9r:r)rLrrNrOrPrQresults rU_matmul_vector_bsr_base._matmul_vectorsujjnn$**Q-vdjj%++/NO14q KKtyy'8   rcc URup#URupEURSn[R"XF4[ UR UR 5S9n[ XB-XS-XbUURURURR5UR5UR55 U$)Nrr,) r'r/r?r@rr-rr8r9r:r)rLrrPrQrNrOn_vecsrs rU_matmul_multivector_bsr_base._matmul_multivectorsnnjjQ1*F4::ekk,JKAD!$1 T\\499??+< v||~ / rccURup#URupEURupgURS:XaURSnOSnURS:XaUS:XaURXx4SS9nOURXx4S9nUR UR UR UR UR 45n [X&-XX-UR RU 5UR RU 5UR RU 5UR RU 55n UR UR UR UR UR 4U S9n [R"UR RU S9n [R"XS9n [R"Xh-U -[URUR5S9n [XU-XX-XhUUR RU 5UR RU 5[R"UR5UR RU 5UR RU 5[R"UR5U U U 5 U R!S Xh5n UR#XU 4X%4Xh4S 9$) Nr"rcsrF)r'r2r&r*r,r[)r/r')r/r'r6r7rBr8r9rrJr?rrr-rrr:reshape_bsr_container)rLrrNK1K2rOrPnrQrRbnnzr8r9r:s rU_matmul_sparse_bsr_base._matmul_sparse)sT  nn <<5 "AA <<5 Q!VKK1%eK!%!4!4Y!?!&!4!4Y!?!&!5!5i!@ B ))4;; +0<<+H15*7 $++++9=((41xxDtzz%++(FG4AqtQ1;;%%i0<<&&y188DII&<<&&y1=='' 288EJJ' ||Bq#"" F #A6aV#  rccUSUR4;aUR5RUS9$U(aUR5$U$)aConvert this array/matrix into Block Sparse Row Format. With copy=False, the data/indices may be shared between this array/matrix and the resultant bsr_array/bsr_matrix. If blocksize=(R, C) is provided, it will be used for determining block size of the bsr_array/bsr_matrix. Nr&)r'tocsrr7r2)rLr'r2s rUr7_bsr_base.tobsrasC T4>>2 2::<%% %: : 99; Krcc 2URup#URupEURnURURUR 4[ Xc5S9n[R"US-US9n[R"XgS9n [R"U[UR5S9n [X$-X5-XEURRUSS9UR RUSS9URUU U 5 URXU4URS9$)Nr*rr,Fr3r/)r/r'r_rBr8r9rCr?rrr-rrJr:_csr_container) rLr2rNrOrPrQr_rRr8r9r:s rUr_bsr_base.tocsrqszz~~hh))4;; *E14S*> !a%y1((30xx6$**#56!&&++$$YU$;,,%%ie%<)) ""D6#:$**"MMrcc:URSS9RUS9$r)rtocsc)rLr2s rUr_bsr_base.tocscs!zzuz%+++66rccURup#URupE[R"UR5nUR R [R "[R5R :aIUR[R5n[R"Xv:g5(a [S5eUnUR[X#5S9nU[R"X$-US9-RU5n U RXE-5RSXE5n U [R "[R"XHS9RSS5SU45- n U RS5n XPR"-RUSS9RXE-5RSXE5n U [R "[R"XXS9US45- n U RS5n UR$RS5n U(aU R'5n UR)XU 44URS9$) zConvert this array/matrix to COOrdinate format. When copy=False the data array will be shared between this array/matrix and the resultant coo_array/coo_matrix. zMatrix too big to convertr*r,r[rFr3r)r/r'r?rar8r-itemsizeintprJanyr>rBrCarangerepeatrtiler9r:r2rE) rLr2rNrOrPrQ indptr_diffindptr_diff_limitedrRrowcolr:s rUr_bsr_base.tocoosjjnnggdkk*    % %(9(B(B B"-"4"4RWW"= vv)899 !<==-K))Q); 299QT33;;KHjjo%%b- rwwryy4<r'r/r_rr-r?rr8r9r:rr) rLaxesr2rPrQrNrONBLKr8r9r:s rU transpose_bsr_base.transposes5  LM M~~zzxx!# 88q=&&v!-1ZZd'D D!$($++*;*;<((4||'9'9:xx! DIIOO<adAD!kk4<<1Btzz| 5""D6#:*+d#< r9r:)rLrPrQrNrOrs rUr^_bsr_base.prunesnnjj t{{ qtax '?@ @{{2 t|| t #AB B tyy>D >? ?IIet$ ||ET* rccURXRS9n[[URU-UR-5nURupg[ UR 5[ UR 5-nURURURURUR4US9n [R"URRU S9n [R"XS9n /SQn X,;a)[R"Xg-U-[RS9n O8[R"Xg-U-[URUR5S9n U"URSU-URSU-XgURR!U 5URR!U 5UR URR!U 5URR!U 5[R""UR 5U U U 5 U SnU SUn U SXg-U-n XS - :a U R%5n U R%5n U R'SXg5n URXU 4URS 9$) z5Apply the binary operation fn to two sparse matrices.r&r*r,)_ne__lt__gt__le__ge_rrr[Nr.r) __class__r'getattrrr6rDr:rBr8r9r?rr/bool_rr-rJrr2r)rLropin_shape out_shapefnrPrQmax_bnnzrRr8r9bool_opsr: actual_bnnzs rU_binopt_bsr_base._binopt.s  u?\4;;#3dkk#A Bnntyy>C O3))4;; +0<<+H19*; $++++9=((85; >88ACL9D88ACLtzz%++0NOD 4::a=! TZZ]A-q ;;  i ( <<  y ) 99 <<  y ) ==   * 88EJJ     Rj ,;'$QS_% ! #llnG99;D||Bq#~~tf5TZZ~HHrcc4U(aWURXRR5URR54URUR S9$URXRUR4URUR S9$)zReturns a matrix with the same sparsity structure as self, but with different data. By default the structure arrays (i.e. .indptr and .indices) are copied. )r/r-)rr9r2r8r/r-)rLr:r2s rU _with_data_bsr_base._with_data_s >>4 (9(9(;DKK>4 T[["A)-$**"F Frcc0[R"XU5$ru)r _broadcast_to)rLr/r2s rUr_bsr_base._broadcast_tors$$T$77rc)r;r:rrr9r8)NNFN)Trur()NF)F)NN)$__name__ __module__ __qualname____firstlineno___formatr5rKpropertyr=r'rkr__doc__rnrrrrrrrrr7rrrrrrrrr^rrr__static_attributes__rcrUr r sGG:?s,-1s,j<-B#5##,oo--GO6$1199M  ''//H ""8  2 p N*MM))EM7MM))EM$ LDoo--GO<2 ))11I ()B '+*.Ib F&8rcr c"[U[5$)aIs `x` of a bsr_matrix type? Parameters ---------- x object to check for being a bsr matrix Returns ------- bool True if `x` is a bsr matrix, False otherwise Examples -------- >>> from scipy.sparse import bsr_array, bsr_matrix, csr_matrix, isspmatrix_bsr >>> isspmatrix_bsr(bsr_matrix([[5]])) True >>> isspmatrix_bsr(bsr_array([[5]])) False >>> isspmatrix_bsr(csr_matrix([[5]])) False )r<r)rss rUrrvs. a $$rcc\rSrSrSrSrg)ria Block Sparse Row format sparse array. This can be instantiated in several ways: bsr_array(D, [blocksize=(R,C)]) where D is a 2-D ndarray. bsr_array(S, [blocksize=(R,C)]) with another sparse array or matrix S (equivalent to S.tobsr()) bsr_array((M, N), [blocksize=(R,C), dtype]) to construct an empty sparse array with shape (M, N) dtype is optional, defaulting to dtype='d'. bsr_array((data, ij), [blocksize=(R,C), shape=(M, N)]) where ``data`` and ``ij`` satisfy ``a[ij[0, k], ij[1, k]] = data[k]`` bsr_array((data, indices, indptr), [shape=(M, N)]) is the standard BSR representation where the block column indices for row i are stored in ``indices[indptr[i]:indptr[i+1]]`` and their corresponding block values are stored in ``data[ indptr[i]: indptr[i+1] ]``. If the shape parameter is not supplied, the array dimensions are inferred from the index arrays. 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 BSR format data array of the array indices BSR format index array of the array indptr BSR format index pointer array of the array blocksize Block size has_sorted_indices : bool Whether indices are sorted has_canonical_format : bool T Notes ----- Sparse arrays can be used in arithmetic operations: they support addition, subtraction, multiplication, division, and matrix power. **Summary of BSR format** The Block Sparse Row (BSR) format is very similar to the Compressed Sparse Row (CSR) format. BSR is appropriate for sparse matrices with dense sub matrices like the last example below. Such sparse block matrices often arise in vector-valued finite element discretizations. In such cases, BSR is considerably more efficient than CSR and CSC for many sparse arithmetic operations. **Blocksize** The blocksize (R,C) must evenly divide the shape of the sparse array (M,N). That is, R and C must satisfy the relationship ``M % R = 0`` and ``N % C = 0``. If no blocksize is specified, a simple heuristic is applied to determine an appropriate blocksize. **Canonical Format** In canonical format, there are no duplicate blocks and indices are sorted per row. **Limitations** Block Sparse Row format sparse arrays do not support slicing. Examples -------- >>> import numpy as np >>> from scipy.sparse import bsr_array >>> bsr_array((3, 4), dtype=np.int8).toarray() array([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], dtype=int8) >>> row = np.array([0, 0, 1, 2, 2, 2]) >>> col = np.array([0, 2, 2, 0, 1, 2]) >>> data = np.array([1, 2, 3 ,4, 5, 6]) >>> bsr_array((data, (row, col)), shape=(3, 3)).toarray() array([[1, 0, 2], [0, 0, 3], [4, 5, 6]]) >>> indptr = np.array([0, 2, 3, 6]) >>> indices = np.array([0, 2, 2, 0, 1, 2]) >>> data = np.array([1, 2, 3, 4, 5, 6]).repeat(4).reshape(6, 2, 2) >>> bsr_array((data,indices,indptr), shape=(6, 6)).toarray() array([[1, 1, 0, 0, 2, 2], [1, 1, 0, 0, 2, 2], [0, 0, 0, 0, 3, 3], [0, 0, 0, 0, 3, 3], [4, 4, 5, 5, 6, 6], [4, 4, 5, 5, 6, 6]]) rNrrrrrrrrcrUrrkrcrc\rSrSrSrSrg)ria Block Sparse Row format sparse matrix. This can be instantiated in several ways: bsr_matrix(D, [blocksize=(R,C)]) where D is a 2-D ndarray. bsr_matrix(S, [blocksize=(R,C)]) with another sparse array or matrix S (equivalent to S.tobsr()) bsr_matrix((M, N), [blocksize=(R,C), dtype]) to construct an empty sparse matrix with shape (M, N) dtype is optional, defaulting to dtype='d'. bsr_matrix((data, ij), [blocksize=(R,C), shape=(M, N)]) where ``data`` and ``ij`` satisfy ``a[ij[0, k], ij[1, k]] = data[k]`` bsr_matrix((data, indices, indptr), [shape=(M, N)]) is the standard BSR representation where the block column indices for row i are stored in ``indices[indptr[i]:indptr[i+1]]`` and their corresponding block values are stored in ``data[ indptr[i]: indptr[i+1] ]``. If the shape parameter is not supplied, the matrix dimensions are 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 BSR format data array of the matrix indices BSR format index array of the matrix indptr BSR format index pointer array of the matrix blocksize Block size has_sorted_indices : bool Whether indices are sorted has_canonical_format : bool T Notes ----- Sparse matrices can be used in arithmetic operations: they support addition, subtraction, multiplication, division, and matrix power. **Summary of BSR format** The Block Sparse Row (BSR) format is very similar to the Compressed Sparse Row (CSR) format. BSR is appropriate for sparse matrices with dense sub matrices like the last example below. Such sparse block matrices often arise in vector-valued finite element discretizations. In such cases, BSR is considerably more efficient than CSR and CSC for many sparse arithmetic operations. **Blocksize** The blocksize (R,C) must evenly divide the shape of the sparse matrix (M,N). That is, R and C must satisfy the relationship ``M % R = 0`` and ``N % C = 0``. If no blocksize is specified, a simple heuristic is applied to determine an appropriate blocksize. **Canonical Format** In canonical format, there are no duplicate blocks and indices are sorted per row. **Limitations** Block Sparse Row format sparse matrices do not support slicing. Examples -------- >>> import numpy as np >>> from scipy.sparse import bsr_matrix >>> bsr_matrix((3, 4), dtype=np.int8).toarray() array([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], dtype=int8) >>> row = np.array([0, 0, 1, 2, 2, 2]) >>> col = np.array([0, 2, 2, 0, 1, 2]) >>> data = np.array([1, 2, 3 ,4, 5, 6]) >>> bsr_matrix((data, (row, col)), shape=(3, 3)).toarray() array([[1, 0, 2], [0, 0, 3], [4, 5, 6]]) >>> indptr = np.array([0, 2, 3, 6]) >>> indices = np.array([0, 2, 2, 0, 1, 2]) >>> data = np.array([1, 2, 3, 4, 5, 6]).repeat(4).reshape(6, 2, 2) >>> bsr_matrix((data,indices,indptr), shape=(6, 6)).toarray() array([[1, 1, 0, 0, 2, 2], [1, 1, 0, 0, 2, 2], [0, 0, 0, 0, 3, 3], [0, 0, 0, 0, 3, 3], [4, 4, 5, 5, 6, 6], [4, 4, 5, 5, 6, 6]]) rNr rrcrUrrr rcr))r __docformat____all__warningsrnumpyr?scipy._lib._utilr_matrixr _datar r _compressedr _baser rrr_sputilsrrrrrrrrrrrrrrr rrrrrcrUrs}(% 7+.#77$$&&& [ 8 M[ 8|%6l 7l^l9lrc