(ph+SrSr/SQrSSKrSSKJr SSKJrJ r SSK J r J r SS K Jr SS KJr "S S \5rS r"SS\\ 5r"SS\\5rg)z&Compressed Sparse Column matrix formatzrestructuredtext en) csc_array csc_matrixisspmatrix_cscN)spmatrix)_spbasesparray) csc_tocsr expandptr)upcast) _cs_matrixcZ\rSrSrSrSSjr\R R\lSrSSjr \RR\ lSSjr \RR\ lSr \ RR\ lS r S rS rS rS rSrSrSr\S5rSrg) _csc_basecscNcUbUS:wa [S5eURup4URURURUR 4XC4US9$)N)rrzvSparse arrays/matrices do not support an 'axes' parameter because swapping dimensions is the only logical permutation.copy) ValueErrorshape_csr_containerdataindicesindptr)selfaxesrMNs D/var/www/html/venv/lib/python3.13/site-packages/scipy/sparse/_csc.py transpose_csc_base.transposesi  LM Mzz""DIIt||$(KK$134&t#E Ec#@# UR5ShvN gN7fN)tocsr)rs r__iter___csc_base.__iter__!s::<s c4U(aUR5$U$r$r)rrs rtocsc_csc_base.tocsc$s 99; Kr"c JURup#URURUR4[ UR U5S9n[ R"US-US9n[ R"UR US9n[ R"UR [UR5S9n[X#URRU5URRU5URUUU5 URXvU4URSS9nSUlU$)N)maxvalrdtypeF)rrT)r_get_index_dtyperrmaxnnznpemptyr r.r astyperrhas_sorted_indices) rrrr idx_dtyperrrAs rr%_csc_base.tocsr,sjj))4;; *E+.txx+;*= !a%y1((48895xxtzz(:;!++$$Y/,,%%i0))      F #**5   $r"czURUR5upURn[R"[ U5URR S9n[XRU5 URXC45upVURS:gnXWnXgn[R"USS9nXXnXhnXV4$)Nr-r mergesort)kind) _swaprrr2r3lenr.r rrargsort) r major_dim minor_dim minor_indices major_indicesrowcolnz_maskinds rnonzero_csc_base.nonzeroEs $zz$**5  ]!34<<;M;MN )[[-8::}<=))q.lljj;/hhxr"cURup#[U5nUS:aX- nUS:dX:a[SU-5eURUS9R 5$)zMReturns a copy of row i of the matrix, as a (1 x n) CSR matrix (row vector). rindex (%d) out of rangeminor)rint IndexError_get_submatrixr%rirrs r_getrow_csc_base._getrow^s_zz F q5 FA q5AF6:; ;"""+1133r"cURup#[U5nUS:aX- nUS:dX:a[SU-5eURUSS9$)zSReturns a copy of column i of the matrix, as a (m x 1) CSC matrix (column vector). rrJT)majorr)rrMrNrOrPs r_getcol_csc_base._getcoljsXzz F q5 FA q5AF6:; ;"""66r"c>URU5RUS9$)NrK)_major_index_fancyrOrrCrDs r_get_intXarray_csc_base._get_intXarrayvs!&&s+:::EEr"c~URS;aURX!SS9$URU5RUS9$)NrNTrUrLrrK)steprO _major_slicerZs r_get_intXslice_csc_base._get_intXsliceysC 88y &&S$&G G  %4434??r"c~URS;aURX!SS9$URUS9RU5$)Nr^Tr_rU)r`rO _minor_slicerZs r_get_sliceXint_csc_base._get_sliceXint~sC 88y &&S$&G G"""-::3??r"cBURU5RU5$r$)rYrfrZs r_get_sliceXarray_csc_base._get_sliceXarrays&&s+88==r"cURUS9RU5nURS:aURUR5$U$)Nrer)rO_minor_index_fancyndimreshaper)rrCrDress r_get_arrayXint_csc_base._get_arrayXintsC!!!,??D 88a<;;syy) ) r"cBURU5RU5$r$)rarmrZs r_get_arrayXslice_csc_base._get_arrayXslices  %88==r"cUSUS4$)zBswap the members of x if this is a column-oriented matrix rrxs rr<_csc_base._swapstQqTzr"rw)NF)F)__name__ __module__ __qualname____firstlineno___formatr r__doc__r&r)r%rGr rRrVr[rbrgrjrqrt staticmethodr<__static_attributes__rwr"rrrsG E ))11I  MM))EM.MM))EM.!((00GO 4 7F@ @ > > r"rc"[U[5$)aIs `x` of csc_matrix type? Parameters ---------- x object to check for being a csc matrix Returns ------- bool True if `x` is a csc matrix, False otherwise Examples -------- >>> from scipy.sparse import csc_array, csc_matrix, coo_matrix, isspmatrix_csc >>> isspmatrix_csc(csc_matrix([[5]])) True >>> isspmatrix_csc(csc_array([[5]])) False >>> isspmatrix_csc(coo_matrix([[5]])) False ) isinstancerrxs rrrs. a $$r"c\rSrSrSrSrg)ra Compressed Sparse Column array. This can be instantiated in several ways: csc_array(D) where D is a 2-D ndarray csc_array(S) with another sparse array or matrix S (equivalent to S.tocsc()) csc_array((M, N), [dtype]) to construct an empty array with shape (M, N) dtype is optional, defaulting to dtype='d'. csc_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]``. csc_array((data, indices, indptr), [shape=(M, N)]) is the standard CSC representation where the row indices for column 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 ---------- 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 CSC format data array of the array indices CSC format index array of the array indptr CSC format index pointer array of the array has_sorted_indices has_canonical_format T Notes ----- Sparse arrays can be used in arithmetic operations: they support addition, subtraction, multiplication, division, and matrix power. Advantages of the CSC format - efficient arithmetic operations CSC + CSC, CSC * CSC, etc. - efficient column slicing - fast matrix vector products (CSR, BSR may be faster) Disadvantages of the CSC format - slow row slicing operations (consider CSR) - changes to the sparsity structure are expensive (consider LIL or DOK) Canonical format - Within each column, indices are sorted by row. - There are no duplicate entries. Examples -------- >>> import numpy as np >>> from scipy.sparse import csc_array >>> csc_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, 2, 2, 0, 1, 2]) >>> col = np.array([0, 0, 1, 2, 2, 2]) >>> data = np.array([1, 2, 3, 4, 5, 6]) >>> csc_array((data, (row, col)), shape=(3, 3)).toarray() array([[1, 0, 4], [0, 0, 5], [2, 3, 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]) >>> csc_array((data, indices, indptr), shape=(3, 3)).toarray() array([[1, 0, 4], [0, 0, 5], [2, 3, 6]]) rwNr{r|r}r~rrrwr"rrr[r"rc\rSrSrSrSrg)ria Compressed Sparse Column matrix. This can be instantiated in several ways: csc_matrix(D) where D is a 2-D ndarray csc_matrix(S) with another sparse array or matrix S (equivalent to S.tocsc()) csc_matrix((M, N), [dtype]) to construct an empty matrix with shape (M, N) dtype is optional, defaulting to dtype='d'. csc_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]``. csc_matrix((data, indices, indptr), [shape=(M, N)]) is the standard CSC representation where the row indices for column 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 ---------- 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 CSC format data array of the matrix indices CSC format index array of the matrix indptr CSC format index pointer array of the matrix has_sorted_indices has_canonical_format T Notes ----- Sparse matrices can be used in arithmetic operations: they support addition, subtraction, multiplication, division, and matrix power. Advantages of the CSC format - efficient arithmetic operations CSC + CSC, CSC * CSC, etc. - efficient column slicing - fast matrix vector products (CSR, BSR may be faster) Disadvantages of the CSC format - slow row slicing operations (consider CSR) - changes to the sparsity structure are expensive (consider LIL or DOK) Canonical format - Within each column, indices are sorted by row. - There are no duplicate entries. Examples -------- >>> import numpy as np >>> from scipy.sparse import csc_matrix >>> csc_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, 2, 2, 0, 1, 2]) >>> col = np.array([0, 0, 1, 2, 2, 2]) >>> data = np.array([1, 2, 3, 4, 5, 6]) >>> csc_matrix((data, (row, col)), shape=(3, 3)).toarray() array([[1, 0, 4], [0, 0, 5], [2, 3, 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]) >>> csc_matrix((data, indices, indptr), shape=(3, 3)).toarray() array([[1, 0, 4], [0, 0, 5], [2, 3, 6]]) rwNrrwr"rrrrr"r)r __docformat____all__numpyr2_matrixr_baserr _sparsetoolsr r _sputilsr _compressedr rrrrrwr"rrs[,% 7#.#D DN%6\ 7\~\9\r"