ó ʽ÷Xc@`sØdZddlmZmZmZdZddgZddlZddl m Z dd l m Z dd l mZdd lm Z dd lmZmZmZmZdd lmZdeefd„ƒYZd„ZdS(s&Compressed Sparse Column matrix formati(tdivisiontprint_functiontabsolute_importsrestructuredtext ent csc_matrixtisspmatrix_cscN(txrangei(tspmatrix(t csc_tocsr(t _sparsetools(tupcastt isintliket IndexMixintget_index_dtype(t _cs_matrixcB`s­eZdZdZd ed„Zejje_d„Zed„Z ej je _ed„Z ej je _d„Z d„Z e j je _d„Zd „Zd „ZRS( sÈ Compressed Sparse Column matrix This can be instantiated in several ways: csc_matrix(D) with a dense matrix or rank-2 ndarray D csc_matrix(S) with another sparse 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 Number of nonzero elements data Data array of the matrix indices CSC format index array indptr CSC format index pointer array has_sorted_indices Whether indices are sorted 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) 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]]) tcsccC`se|dk rtdƒ‚n|j\}}ddlm}||j|j|jf||fd|ƒS(NsoSparse matrices do not support an 'axes' parameter because swapping dimensions is the only logical permutation.i(t csr_matrixtcopy(tNonet ValueErrortshapetcsrRtdatatindicestindptr(tselftaxesRtMtNR((s//tmp/pip-build-7oUkmx/scipy/scipy/sparse/csc.pyt transposeps  cc`sC|jƒ}x0t|jdƒD]}||dd…fVq WdS(Ni(ttocsrRR(RRtr((s//tmp/pip-build-7oUkmx/scipy/scipy/sparse/csc.pyt__iter__~s cC`s|r|jƒS|SdS(N(R(RR((s//tmp/pip-build-7oUkmx/scipy/scipy/sparse/csc.pyttocscƒs c C`s|j\}}t|j|jfdt|j|ƒƒ}tj|dd|ƒ}tj|jd|ƒ}tj|jdt|j ƒƒ}t |||jj |ƒ|jj |ƒ|j |||ƒddl m}||||fd|jƒ} t| _| S(Ntmaxvalitdtype(RR(RR RRtmaxtnnztnptemptyR R"RtastypeRRRtTruethas_sorted_indices( RRRRt idx_dtypeRRRRtA((s//tmp/pip-build-7oUkmx/scipy/scipy/sparse/csc.pyR‹s"!  cC`st|j|ƒ\}}t|tƒsKt|tƒsKt|ƒsKt|ƒr_|j||fjS|j||fSdS(N(t _unpack_indext isinstancetsliceR tT(Rtkeytrowtcol((s//tmp/pip-build-7oUkmx/scipy/scipy/sparse/csc.pyt __getitem__¢s c C`sÉ|j|jƒ\}}|j}tjt|ƒd|jjƒ}tj||j |ƒ|j||fƒ\}}|j dk}||}||}tj |ddƒ}||}||}||fS(NR"itkindt mergesort( t_swapRRR%R&tlenR"Rt expandptrRRtargsort( Rt major_dimt minor_dimt minor_indicest major_indicesR1R2tnz_masktind((s//tmp/pip-build-7oUkmx/scipy/scipy/sparse/csc.pytnonzero®s !    cC`s|j|tdƒƒjƒS(s]Returns a copy of row i of the matrix, as a (1 x n) CSR matrix (row vector). N(t_get_submatrixR.RR(Rti((s//tmp/pip-build-7oUkmx/scipy/scipy/sparse/csc.pytgetrowÇscC`s|jtdƒ|ƒS(scReturns a copy of column i of the matrix, as a (m x 1) CSC matrix (column vector). N(RAR.R(RRB((s//tmp/pip-build-7oUkmx/scipy/scipy/sparse/csc.pytgetcolÏscC`s|d|dfS(sBswap the members of x if this is a column-oriented matrix ii((Rtx((s//tmp/pip-build-7oUkmx/scipy/scipy/sparse/csc.pyR6×sN(t__name__t __module__t__doc__tformatRtFalseRRRR RR3R@R RCRDR6(((s//tmp/pip-build-7oUkmx/scipy/scipy/sparse/csc.pyRsY       cC`s t|tƒS(N(R-R(RE((s//tmp/pip-build-7oUkmx/scipy/scipy/sparse/csc.pyRÜs(RHt __future__RRRt __docformat__t__all__tnumpyR%tscipy._lib.sixRtbaseRRRttsputilsR R R R t compressedR RR(((s//tmp/pip-build-7oUkmx/scipy/scipy/sparse/csc.pyts  "È