a CCCf1+@sdZdZgdZddlZddlmZddlmZm Z ddl m Z m Z dd l mZdd lmZGd d d eZd dZGdddee ZGdddeeZdS)z&Compressed Sparse Column matrix formatzrestructuredtext en) csc_array csc_matrixisspmatrix_cscN)spmatrix)_spbasesparray) csc_tocsr expandptr)upcast) _cs_matrixc@seZdZdZd ddZejje_ddZd!dd Zejje_d"d d Z ej je _d d Z e j je _ddZ ddZ ddZddZddZddZddZddZeddZdS)# _csc_baseZcscNFcCsB|dur|dkrtd|j\}}|j|j|j|jf||f|dS)N)rrzvSparse arrays/matrices do not support an 'axes' parameter because swapping dimensions is the only logical permutation.copy) ValueErrorshape_csr_containerdataindicesindptr)selfZaxesrMNrM/var/www/html/django/DPS/env/lib/python3.9/site-packages/scipy/sparse/_csc.py transposes  z_csc_base.transposeccs|EdHdSN)tocsr)rrrr__iter__!sz_csc_base.__iter__cCs|r |S|SdSrr)rrrrrtocsc$sz_csc_base.tocscc Cs|j\}}|j|j|jft|j|d}tj|d|d}tj|j|d}tj|jt|j d}t |||j ||j ||j ||||j |||f|jdd}d|_|S)N)maxvalrdtypeF)rrT)rZ_get_index_dtyperrmaxZnnznpemptyr r"r ZastyperrZhas_sorted_indices) rrrrZ idx_dtyperrrArrrr,s*    z_csc_base.tocsrc Cs||j\}}|j}tjt||jjd}t||j||||f\}}|j dk}||}||}tj |dd}||}||}||fS)Nr!rZ mergesort)kind) _swaprrr$r%lenr"r rrZargsort) rZ major_dimZ minor_dimZ minor_indicesZ major_indicesrowcolZnz_maskindrrrnonzeroEs z_csc_base.nonzerocCsN|j\}}t|}|dkr"||7}|dks2||kr>td||j|dS)z]Returns 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_submatrixrrirrrrr_getrow^s  z_csc_base._getrowcCsL|j\}}t|}|dkr"||7}|dks2||kr>td||j|ddS)zcReturns a copy of column i of the matrix, as a (m x 1) CSC matrix (column vector). rr.T)majorr)rr1r2r3r4rrr_getcoljs  z_csc_base._getcolcCs||j|dS)Nr/)_major_index_fancyr3rr*r+rrr_get_intXarrayvsz_csc_base._get_intXarraycCs,|jdvr|j||ddS||j|dS)NrNTr7r0rr/)stepr3 _major_slicer:rrr_get_intXsliceys z_csc_base._get_intXslicecCs,|jdvr|j||ddS|j|d|S)Nr<Tr=r7)r>r3 _minor_slicer:rrr_get_sliceXint~s z_csc_base._get_sliceXintcCs|||Sr)r9rBr:rrr_get_sliceXarraysz_csc_base._get_sliceXarraycCs|j|d|S)NrA)r3_minor_index_fancyr:rrr_get_arrayXintsz_csc_base._get_arrayXintcCs|||Sr)r?rEr:rrr_get_arrayXslicesz_csc_base._get_arrayXslicecCs|d|dfS)zBswap the members of x if this is a column-oriented matrix rrrxrrrr(sz_csc_base._swap)NF)F)F)__name__ __module__ __qualname___formatrr__doc__rrrr-r r6r8r;r@rCrDrFrG staticmethodr(rrrrr s(        r cCs t|tS)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 ) isinstancerrHrrrrsrc@seZdZdZdS)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]]) NrJrKrLrNrrrrrsrc@seZdZdZdS)ra? 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]]) NrQrrrrrsr)rN __docformat____all__numpyr$Z_matrixr_baserrZ _sparsetoolsr r Z_sputilsr Z _compressedr r rrrrrrrs   _