+ nDjORtRt.ROt^RIt^RIHt^RIHtH t ^RI H t H t H t HtHt^RIHt^R IHt!R R ]4tR t!R R]] 4t!RR]]4tR#)z#Compressed Sparse Row matrix formatzrestructuredtext en csr_array csr_matrixN)spmatrix)_spbasesparray) csr_tocsc csr_tobsrcsr_count_blocksget_csr_submatrixcsr_sample_values)upcast) _cs_matrixcaa]tRt^toRtRtRRlt]P P]nRRlt ]PP] nRRlt ]PP] nRV3Rllt ]PP] nRRlt ]PP] nRRlt ]PP] n]R4tR tR tR tR tR tRtRtRtRtRtRtRtRtRtRtVtV;t #) _csr_basecsrc VeVR8wd \R4hVP^8XdV'dVP4#T#VPwr4VP VP VP VP3WC3VR7#)NzvSparse arrays/matrices do not support an 'axes' parameter because swapping dimensions is the only logical permutation.shapecopy)) ValueErrorndimrr_csc_containerdataindicesindptr)selfaxesrMNs&&& K/data/cameron/venvs/s3viz/lib/python3.14/site-packages/scipy/sparse/_csr.py transpose_csr_base.transposes  LM M 99>"&499; 0D 0zz""DIIt||$(KK$19:T#K KcVP^8wd \R4hVPVPVPR7pVP 4VP VPVPrTpVPVPrv\VP^,4FBpW8,p W8^,,p WIV P4Wh&WYV P4Wx&KD V#)z.Cannot convert a 1d sparse array to lil formatdtype) rr_lil_containerrr(sum_duplicatesrrrrowsrangetolist) rrlilptrinddatr+rnstartends && r!tolil_csr_base.tolil$s 99>MN N!!$**DJJ!? kk$,,tyyXXsxxdtzz!}%AFEc(Cn++-DGn++-DG &  r$c6V'dVP4#V#Nr)rrs&&r!tocsr_csr_base.tocsr7s 99; Kr$cJ<\SV`VR7pVPVnV#)r9)supertocoohas_canonical_format)rrA __class__s&& r!r>_csr_base.tocoo?s( GMtM $"&!:!:r$c VP^8wd \R4hVPwr#VPVPVP 3\ VPV4R7p\P!V^,VR7p\P!VPVR7p\P!VP\VP4R7p\W#VPPVRR7VP PVRR7VPVVV4VPWvV3VPR7pRVnV#)r&z.Cannot convert a 1d sparse array to csc formatmaxvalr'Fr9rT)rrr_get_index_dtyperrmaxnnznpemptyr r(rastyperrhas_sorted_indices) rrrr idx_dtyperrrr@s && r!tocsc_csr_base.tocscIs 99>MN Nzz))4;; *E+.txx+;*= !a%y1((48895xxtzz(:;!++$$YU$;,,%%ie%<))      7tzz  J#r$c 0VP^8wd \R4hVf^RIHpVP V!V4R7#VR 8XdRVP P R ^^4VPVP3pVPW@PVR7#VwrVVPwrxV^8g"V^8gWu,^8wgW,^8wd\RV 24h\WxWVVPVP4p VPVPVP3\W,V 4R7p \P!Wu,^,V R7p \P!WR7p \P !WV3VP"R7p \%WxWVVPP'V RR 7VPP'V RR 7VP WV P)44 VPWV 3VPR 7#) r&z.Cannot convert a 1d sparse array to bsr format)estimate_blocksize) blocksizerzinvalid blocksize rDr'Fr9rF)rr)rr_spfuncsrRtobsrrreshaperr_bsr_containerrr rGrHrJrKzerosr(rrLravel)rrSrrRarg1RCrr blksrNrrrs&&& r!rV_csr_base.tobsras 99>MN N   4::(:4(@:A A % II%%b1-dll4;;GD&&t::D&I ICA**CA1uA!quz #5i[!ABB#ADKK ED--t{{DLL.I/214.@IXXad1fI6Fhht5G88TAJdjj9D aAkk(((?ll)))%)@iitzz|  5 &&'tzz' r$c V#)zBswap the members of x if this is a column-oriented matrix xs&r!_swap_csr_base._swaps r$c#"VP^8XdVPP^4p^p\VPVP 4F.wr4\ W2, 4FpVxK VxV^,pK0 \ VP^,V, 4FpVxK R#\P!^VPPR7p\V\4'dVPR,M^VP^,3p^pVPR,FDp W, V^&VPWp VP Wp VPWV3VRR7xT pKF R#5i)rNr':rNNTr)rr(typeziprrr,rrJrYr isinstancerrA) rzerouvd_rri0i1rrs & r!__iter___csr_base.__iter__s- 99>::??1%DADLL$))4quAJ&E 5 4::a=1,- . !4;;#4#45",T7";"; 2!TZZPQ]AS ++b//BF1Ill2)G99R#D..$!8D.Q QB "sE:E<c  VP^8Xd=VR9d\RV R24hVP^VP^,3RR7#VPwr#\ V4pV^8d W, pV^8gW8d\RV R24h\ W#VP VPVPW^,^V4 wrEpVPWeV3^V3VPRR7#)zMReturns a copy of row i of the matrix, as a (1 x n) CSR matrix (row vector). index () out of rangeTr9Frr(r)rrT) r IndexErrorrWrintr rrrrAr(rirr rrrs&& r!_getrow_csr_base._getrows 99> 71#^!<==<<DJJqM 2<> >zz F q5 FA q5AFwqc89 9 1 $++t||TYYq5!Q!H~~tf5aV$(JJU< ST Tzz F q5 FA q5AFwqc89 9 1 $++t||TYY1Q!H~~tf5aV$(JJU< >"DJJ>7 7$**Q-1mmC1jj.hhsxxtzz2! T\\499((Cc 3"%1!1CII !  ~~ckk)455r$cBVPV4PV4#r8)r{_minor_index_fancyrrrs&&&r!_get_intXarray_csr_base._get_intXarrays||C 33C88r$c VPR9dVPWRR7#VPwr4VPV4wrVpVPW^,wrVPWp VP Wp V^8dW8W8,p M W8*W8,p \ V4^8dWV, V,^8H,p W,V, V,p W,p \P!^\V 4.4p V^8d"V RRR1,p \ V RRR1,4p ^\^\\P!\We, 4V, 4443pVPWV 3VVPRR7#)rNTr9FrvrrT)rrrrrrabsrJarraylenrHrxceilfloatrAr()rrrrr r3stopstrideiijj row_indicesrow_datar0 row_indptrrs&&& r!_get_intXslice_csr_base._get_intXslicesg 88y &&sd&; ;zz!kk!nVSQ'll2) 99R# A:'K,>?C'K,>?C v;? %'61Q6 6C"'%/F: =XXq#k"234 A:"~Hk$B$/0KC3rwwuT\':V'CDEFG~~xjA$(JJU< rOrV staticmethodrdrqr{r~rrrrrrrrrrr__static_attributes____classdictcell__ __classcell__)rA __classdict__s@@r!rrsGI K ))11I"MM))EM MM))EMMM))EM,MM))EM"HMM))EM 0<(< ' &6 9 F "33r$rc"\V\4#)a0Is `x` of csr_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 csr matrix Returns ------- bool True if `x` is a csr matrix, False otherwise Examples -------- >>> from scipy.sparse import csr_array, csr_matrix, coo_matrix, isspmatrix_csr >>> isspmatrix_csr(csr_matrix([[5]])) True >>> isspmatrix_csr(csr_array([[5]])) False >>> isspmatrix_csr(coo_matrix([[5]])) False )rirrbs&r!isspmatrix_csrr)s@ a $$r$c]tRtRtRtRtR#)riMa Compressed Sparse Row array. This can be instantiated in several ways: csr_array(D) where D is a 2-D ndarray csr_array(S) with another sparse array or matrix S (equivalent to S.tocsr()) csr_array((M, N), [dtype]) to construct an empty array with shape (M, N) dtype is optional, defaulting to dtype='d'. csr_array((data, (row_ind, col_ind)), [shape=(M, N)]) where ``data``, ``row_ind`` and ``col_ind`` satisfy the relationship ``a[row_ind[k], col_ind[k]] = data[k]``. csr_array((data, indices, indptr), [shape=(M, N)]) is the standard CSR representation where the column indices for row i are stored in ``indices[indptr[i]:indptr[i+1]]`` and their corresponding 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 CSR format data array of the array indices : ndarray CSR format index array of the array indptr : ndarray CSR format index pointer array of the array 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. 'csr' nnz : int Number of values stored in the array size : int Number of values stored in the array T : csr_array The transpose of the array mT : csr_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. Advantages of the CSR format - efficient arithmetic operations CSR + CSR, CSR * CSR, etc. - efficient row slicing - fast matrix vector products Disadvantages of the CSR format - slow column slicing operations (consider CSC) - changes to the sparsity structure are expensive (consider LIL or DOK) Canonical Format - Within each row, indices are sorted by column. - There are no duplicate entries. Examples -------- >>> import numpy as np >>> from scipy.sparse import csr_array >>> csr_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]) >>> csr_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]) >>> csr_array((data, indices, indptr), shape=(3, 3)).toarray() array([[1, 0, 2], [0, 0, 3], [4, 5, 6]]) Duplicate entries are summed together: >>> row = np.array([0, 1, 2, 0]) >>> col = np.array([0, 1, 1, 0]) >>> data = np.array([1, 2, 4, 8]) >>> csr_array((data, (row, col)), shape=(3, 3)).toarray() array([[9, 0, 0], [0, 2, 0], [0, 4, 0]]) As an example of how to construct a CSR array incrementally, the following snippet builds a term-document array from texts: >>> docs = [["hello", "world", "hello"], ["goodbye", "cruel", "world"]] >>> indptr = [0] >>> indices = [] >>> data = [] >>> vocabulary = {} >>> for d in docs: ... for term in d: ... index = vocabulary.setdefault(term, len(vocabulary)) ... indices.append(index) ... data.append(1) ... indptr.append(len(indices)) ... >>> csr_array((data, indices, indptr), dtype=int).toarray() array([[2, 1, 0, 0], [0, 1, 1, 1]]) raNrrrrrrrar$r!rrMs@r$c]tRtRtRtRtR#)riah Compressed Sparse Row 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: csr_matrix(D) where D is a 2-D ndarray csr_matrix(S) with another sparse array or matrix S (equivalent to S.tocsr()) csr_matrix((M, N), [dtype]) to construct an empty matrix with shape (M, N) dtype is optional, defaulting to dtype='d'. csr_matrix((data, (row_ind, col_ind)), [shape=(M, N)]) where ``data``, ``row_ind`` and ``col_ind`` satisfy the relationship ``a[row_ind[k], col_ind[k]] = data[k]``. csr_matrix((data, indices, indptr), [shape=(M, N)]) is the standard CSR representation where the column indices for row i are stored in ``indices[indptr[i]:indptr[i+1]]`` and their corresponding 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 CSR format data array of the matrix indices : ndarray CSR format index array of the matrix indptr : ndarray CSR format index pointer array of the matrix 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. 'csr' nnz : int Number of values stored in the matrix size : int Number of values stored in the matrix T : csr_matrix The transpose of the matrix mT : csr_matrix The matrix transpose Notes ----- Sparse matrices can be used in arithmetic operations: they support addition, subtraction, multiplication, division, and matrix power. Advantages of the CSR format - efficient arithmetic operations CSR + CSR, CSR * CSR, etc. - efficient row slicing - fast matrix vector products Disadvantages of the CSR format - slow column slicing operations (consider CSC) - changes to the sparsity structure are expensive (consider LIL or DOK) Canonical Format - Within each row, indices are sorted by column. - There are no duplicate entries. Examples -------- >>> import numpy as np >>> from scipy.sparse import csr_matrix >>> csr_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]) >>> csr_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]) >>> csr_matrix((data, indices, indptr), shape=(3, 3)).toarray() array([[1, 0, 2], [0, 0, 3], [4, 5, 6]]) Duplicate entries are summed together: >>> row = np.array([0, 1, 2, 0]) >>> col = np.array([0, 1, 1, 0]) >>> data = np.array([1, 2, 4, 8]) >>> csr_matrix((data, (row, col)), shape=(3, 3)).toarray() array([[9, 0, 0], [0, 2, 0], [0, 4, 0]]) As an example of how to construct a CSR matrix incrementally, the following snippet builds a term-document matrix from texts: >>> docs = [["hello", "world", "hello"], ["goodbye", "cruel", "world"]] >>> indptr = [0] >>> indices = [] >>> data = [] >>> vocabulary = {} >>> for d in docs: ... for term in d: ... index = vocabulary.setdefault(term, len(vocabulary)) ... indices.append(index) ... data.append(1) ... indptr.append(len(indices)) ... >>> csr_matrix((data, indices, indptr), dtype=int).toarray() array([[2, 1, 0, 0], [0, 1, 1, 1]]) raNrrar$r!rrsIr$)rrr)r __docformat____all__numpyrJ_matrixr_baserr _sparsetoolsrrr r r _sputilsr _compressedr rrrrrar$r!rsc)% 7#AA#T3 T3n %HA 7AHJ9Jr$