+ nDj~RtRt.ROt^RIHt^RIt^RIHt^RI H t ^RI H t H t ^R IHt^R IHtHtHtHt^R IHtHtHtHtHtHt^R IHt^R IHtHtH t H!t!H"t"H#t#H$t$!RR]] 4t%Rt&!RR]%]4t'!RR] ]%4t(R#)z"Compressed Block Sparse Row formatzrestructuredtext en bsr_array bsr_matrix)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_tocsrca]tRt^toRtR RR/RlltR!Rlt]V3RlRl4tR"Rlt ] PP] n R"R lt ] PP] n R t R#R lt] PP]n R tR tRtRtRtRtR$Rlt] P*P]n R%Rlt] P,P]n R%Rlt] P.P]n R!Rlt] P0P]n R&Rlt] P2P]n R$Rlt] P4P]n RtRtRtRtR&RltR!Rlt R%Rlt!Rt"Vt#R#)' _bsr_basebsrNmaxprintc \P!WVR7\V4'dVPVP8XdV'dVP 4pMVP VR7pVP VPVPVP3wVnVnVnVn EM\V\4'Ed \V4'Ed\V4Vn VPwrxVfRpM*\V4'g\RV 24h\V4p\ P"!RV,\%V\&R74VnVwrWy,^8wgW,^8wd \R4hVP)\+Wy,W,W4R7p \ P"!^V R7Vn\ P"!Wy,^,V R7VnEM\-V4^8XdoVP/WVR7p V P VR7p V P V PV PV P3wVnVnVnVn EM3\-V4^8XEdXVwrp^pVe \+V4pVe\+V\+V44pVP)VV3VR R 7p V'g\0p\ P2!WV R 7Vn\ P2!VWKR 7Vn\5WVR 7VnVPP6^8wd#\R VPP 24hVew\V4'g\RV 24h\V4VPPR ,8wd-\RV RVPPR , 24hM\R4h\ P8!V4p\T\<4'd+TP6^8wd\RTP6 R24hTP/YR7P TR7pTP TPTPTP3wTnTnTnTn Ve\V4Vn MxVPfk\-VP 4^, pVPP+4^,pVP>wr\Wy,W,34Vn VPf Vf \R4h\V4Vn Ve6VPPA\%W0P4RR7VnVPCRR7R# \:d p\RTP R24ThRp?ii;i \:dp\R4ThRp?ii;i))r blocksizeNzinvalid blocksize=)defaultz#shape must be multiple of blocksizemaxvaldtype)r'shapeT)r%check_contents)copyr'z*BSR data must be 3-dimensional, got shape=NNzmismatching 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 shapeFr*) full_check)r,r,)"r__init__r formatr*tobsrindptrindicesdata_shape isinstancetuplerrr( ValueErrornpzerosrfloat_get_index_dtypemaxlen_coo_containerrarrayrndimasarray Exceptionr r"astype check_format)selfarg1r(r'r*r"rMNRC idx_dtypecoorr6r5r4r%es&&&&&&$ K/data/cameron/venvs/s3viz/lib/python3.14/site-packages/scipy/sparse/_bsr.pyr1_bsr_base.__init__sd8< D>>{{dkk)dyy{zzIz6 T\\499dkkA =DKty$+U # #t}})$/ jj$ %I"9--(+=i[)IJJ %i 0IHHTI%5xu7UV Ea>Q&$DTYY__DUV("9--(+=i[)IJJY'499??2+>>(4YK"499??2#6"79 !!KLL Mzz$'$((TYY!^ #= 0z8index pointer values must form a non-decreasing sequencer&N)r(r"r4r'kindrnamer5rCr:r6r@prunennzr?minr;diffr>rDr)rHr.rJrKrLrMrNs&& rQrG_bsr_base.check_formatsjjnn ;;   ! !S ( 7 8I8I8N8N7OqQ  <<   " "c ) 89K9K9P9P8QQRS  <<   !T[[%5%5%:@A A 99>>Q 12 2  q (&s4;;'7&8 adQhZqQ  KKNa @A A  TYY /IJ J KKOc$,,/ /DE E :xx!|<<##%-$8$T\\%5%5%7$8;<<##%)$%GHH774;;'++-1$&BCC--t||T[[.IJI**T[[ BDK::dll)DDL!$)),DI# c <V^8dQhRS[/#)return)r9)r2 __classdict__s"rQ __annotate___bsr_base.__annotate__s##5#r_c <VPPR,#)zBlock size of the matrix.r+)r6r()rHs&rQr"_bsr_base.blocksizesyyr""r_cVe \R4hVPwr#\VPR,4V,V,#)Nz6_getnnz over an axis is not implemented for BSR formatrW)NotImplementedErrorr"intr4)rHaxisrLrMs&& rQ_getnnz_bsr_base._getnnzsB  %'78 8~~4;;r?#a'!++r_cVe!VP4PVR7#\P!VP44#)Nrk)tocsr count_nonzeror; _deduped_data)rHrks&&rQrq_bsr_base.count_nonzeros<  ::<--4-8 8 2 2 455r_c \VP,wr\V\4'dRMRpRP RVP 44pRV RV RVP RVP RV R VP R 2 #) rBmatrixxc38"TFp\V4xK R#5iN)str).0rvs& rQ %_bsr_base.__repr__..s4^SVV^s) r r2r8r joinr"r'r[r()rH_fmt sparse_clsbs& rQ__repr___bsr_base.__repr__s$++& *4 9 9Wx HH4T^^4 4uHZL DJJ<@hhZ;A3l4::,VW Y r_c VPwr#W)8:gW8d-\P!^VPPR7#VP wrE\P !\V\V^4,V\V^4, 4\VP4R7p\P!WV,W5,WEVPVP\P!VP4V4 V#r0r&)r(r;emptyr6r'r"r<r\r?rr bsr_diagonalr4r5ravel)rHkrowscolsrLrMys&& rQdiagonal_bsr_base.diagonalsZZ  :88ATYY__5 5~~ HHSAq )4#a)+;<!$**- /!!!QY 1"&++t||"$((499"5q :r_c\hrxri)rHkeys&&rQ __getitem___bsr_base.__getitem__!!r_c\hrxr)rHrvals&&&rQ __setitem___bsr_base.__setitem__rr_cDVPRR7PV4#Fr-)tocoo _add_dense)rHothers&&rQr_bsr_base._add_denseszzuz%0077r_c ^VPwr#VPwrE\P!VP^,\ VP VP 4R7p\ W$,W5,WEVPVPVPP4W4 V#r) r(r"r;r<rr'rr4r5r6r)rHrrJrKrLrMresults&& rQ_matmul_vector_bsr_base._matmul_vectorsujjnn$**Q-vdjj%++/NO14q KKtyy'8   r_c VPwr#VPwrEVP^,p\P!WF3\ VP VP 4R7p\ WB,WS,WbVVPVPVPP4VP4VP44 V#)r,r&) r"r(r;r<rr'rr4r5r6r)rHrrLrMrJrKn_vecsrs&& rQ_matmul_multivector_bsr_base._matmul_multivectorsnnjjQ1*F4::ekk,JKAD!$1 T\\499??+< v||~ / r_cVPwr#VPwrEVPwrgVPR8XdVP^,pM^pVPR8XdV^8XdVPWx3RR7pMVPWx3R7pVP VP VP VP VP 34p W&,p WX,p \WVP PV RR7VP PV RR7VP PV RR7VP PV RR74p VP VP VP VP VP 3V R7p \P!VP PV R7p \P!WR7p\P!Wh,V ,\VPVP4R7p\WWWVP PV RR7VP PV RR7\P!VP4VP PV RR7VP PV RR7\P!VP4V VV4VP!R Wh4pVP#WV 3W%3Wh3R7#) rcsrF)r"r*r!r-r$r&)r(r"rW)r(r"r2r3r>r4r5rrFr;rrr'rrr6reshape_bsr_container)rHrrJK1K2rKrLnrMrNn_brown_bcolbnnzr4r5r6s&& rQ_matmul_sparse_bsr_base._matmul_sparse*s|  nn <<5 "AA <<5 Q!VKK1%eK88EJJ' ||Bq#"" F #A6aV#  r_cVRVP39d!VP4PVR7#V'dVP4#V#)Nr!)r"rpr3r*)rHr"r*s&&&rQr3_bsr_base.tobsrdsA T4>>2 2::<%% %: : 99; Kr_c tVPwr#VPwrEVPpVPVPVP 3\ Wc4R7p\P!V^,VR7p\P!WgR7p \P!V\VP4R7p \W$,W5,WEVPPVRR7VP PVRR7VPVV V 4 VPWV3VPR7#)r$r&Fr-r()r(r"r[r>r4r5r?r;rrr'rrFr6_csr_container) rHr*rJrKrLrMr[rNr4r5r6s && rQrp_bsr_base.tocsrnszz~~hh))4;; *E14S*> !a%y1((30xx6$**#56!&&++$$YU$;,,%%ie%<)) ""D6#:$**"MMr_cFVPRR7PVR7#r)rptocsc)rHr*s&&rQr_bsr_base.tocscs!zzuz%+++66r_cVPwr#VPwrE\P!VP4pVP P \P !\P4P 8dKVP\P4p\P!Wv8g4'd \R4hTpVP\W#4R7pV\P!W$,VR7,PV4p V PWE,4PRWE4p V \P !\P!WHR7PR^4^V34, p V PR4p WPP",PVRR7PWE,4PRWE4p V \P !\P!WXR7V^34, p V PR4p VP$PR4p V'dV P'4p VP)WV 33VPR7#)zMatrix too big to convertr$r&Fr-rrW)r(r"r;r]r4r'itemsizeintprFanyr:r>r?arangerepeatrtiler5r6r*rA) rHr*rJrKrLrM indptr_diffindptr_diff_limitedrNrowcolr6s && rQr_bsr_base.tocoosjjnnggdkk*    % %(9(B(B B"-"4"4RWW"= vv)899 !<==-K))Q); 299QT33;;KHjjo%%b- rwwryy4<D >? ?IIet$ ||ET* r_c VPWPR7p\\VPV,VP,4pVPwrg\ VP 4\ VP 4,pVPVPVPVPVP3VR7p \P!VPPV R7p \P!WR7p .ROp W,9d5\P!Wg,V,\PR7p MC\P!Wg,V,\VPVP4R7p V!VP^,V,VP^,V,WgVPP!V RR7VPP!V RR7VP VPP!V RR7VPP!V RR7\P"!VP 4V V V 4 V R ,pV RVp V RWg,V,p W^, 8d!V P%4p V P%4p V P'R Wg4p VPWV 3VPR7#) z5Apply the binary operation fn to two sparse matrices.r!r$r&Fr-Nr)_ne__lt__gt__le__ge_rW) __class__r"getattrrr2r@r6r>r4r5r;rr(bool_rr'rFrr*r)rHropin_shape out_shapefnrLrMmax_bnnzrNr4r5bool_opsr6 actual_bnnzs&&&&& rQ_binopt_bsr_base._binopt's! u?\4;;#3dkk#A Bnntyy>C O3))4;; +0<<+H19*; $++++9=((85; >88ACL9D88ACLtzz%++0NOD 4::a=! TZZ]A-q ;;  ie  4 <<  yu  5 99 <<  yu  5 ==     6 88EJJ     Rj ,;'$QS_% ! #llnG99;D||Bq#~~tf5TZZ~HHr_c BV'd[VPWPP4VPP43VPVP R7#VPWPVP3VPVP R7#)zReturns a matrix with the same sparsity structure as self, but with different data. By default the structure arrays (i.e. .indptr and .indices) are copied. )r(r')rr5r*r4r(r')rHr6r*s&&&rQ _with_data_bsr_base._with_dataXs >>4 (9(9(;DKK>4 T[["A)-$**"F Fr_c0\P!WV4#rx)r _broadcast_to)rHr(r*s&&&rQr_bsr_base._broadcast_toks$$T$77r_)r7r6rrr5r4)NNFN)Trxr/)NF)F)NN)$__name__ __module__ __qualname____firstlineno___formatr1rGpropertyr"rlr __doc__rqrrrrrrrrr3rprrrrrrrrZrrr__static_attributes____classdictcell__)rcs@rQrrshGs,-1s,j?-H##,oo--GO6 $1199M  ''//H ""8  4 tMM))EMN*MM))EM7MM))EM @MM))EMDoo--GO<2 ))11I ()B '+*.Ib F&88r_rc"\V\4#)a2Is `x` of a bsr_matrix type? .. warning:: SciPy sparse is shifting from a sparse matrix interface to a sparse array interface. In the next few releases we expect to deprecate the sparse matrix interface. For documentation of the matrix interface, see the :ref:`spmatrix interface docs `. For guidance on converting existing code to sparse arrays, see :ref:`Migration from spmatrix to sparray `. 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 )r8r)rvs&rQisspmatrix_bsrr os@ a $$r_c]tRtRtRtRtR#)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 ---------- data : ndarray BSR format data array of the array indices : ndarray BSR format index array of the array indptr : ndarray BSR format index pointer array of the array blocksize : 2-tuple of integers Block size (R, C) has_sorted_indices : bool Whether indices are sorted has_canonical_format : bool Whether indices are sorted and no duplicate entries exist dtype : dtype Data type of the array shape : 2-tuple Shape of the array ndim : int Number of dimensions (this is always 2) format : str Three letter code for the format of the array storage, e.g. 'bsr' nnz : int Number of values stored in the array size : int Number of values stored in the array T : bsr_array The transpose of the array mT : bsr_array The matrix transpose of the array 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]]) Nrrrrr r rr_rQrrssr_c]tRtRtRtRtR#)ri a Block Sparse Row format sparse matrix. .. warning:: SciPy sparse is shifting from a sparse matrix interface to a sparse array interface. In the next few releases we expect to deprecate the sparse matrix interface. For documentation of the matrix interface, see the :ref:`spmatrix interface docs `. For guidance on converting existing code to sparse arrays, see :ref:`Migration from spmatrix to sparray `. 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 ---------- data : ndarray BSR format data array of the matrix indices : ndarray BSR format index array of the matrix indptr : ndarray BSR format index pointer array of the matrix blocksize : 2-tuple of integers Block size (R, C) has_sorted_indices : bool Whether indices are sorted has_canonical_format : bool Whether indices are sorted and no duplicate entries exist dtype : dtype Data type of the matrix shape : 2-tuple Shape of the matrix ndim : int Number of dimensions (this is always 2) format : str Three letter code for the format of the matrix storage, e.g. 'bsr' nnz : int Number of values stored in the matrix size : int Number of values stored in the matrix T : bsr_matrix The transpose of the matrix mT : bsr_matrix The matrix transpose 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]]) rNrrr_rQrr s|r_)rrr ))r  __docformat____all__warningsrnumpyr;scipy._lib._utilr_matrixr_datarr _compressedr _baser r r r _sputilsrrrrrrrrrrrrrrrr rrrr_rQrs~(% 7+.#77$$&&& T 8 MT 8n %Ht 7tn}9}r_