# publicscipy/scipy

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 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 `"""Compressed Sparse Column matrix format"""__docformat__ = "restructuredtext en"__all__ = ['csc_matrix', 'isspmatrix_csc']from warnings import warnimport numpy as npfrom sparsetools import csc_tocsrfrom sputils import upcast, isintlikefrom compressed import _cs_matrixclass csc_matrix(_cs_matrix):    """ 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, ij), [shape=(M, N)]) where ``data`` and ``ij`` satisfy the relationship ``a[ij[0, k], ij[1, 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 -------- >>> from scipy.sparse import * >>> from scipy import * >>> csc_matrix( (3,4), dtype=int8 ).todense() matrix([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], dtype=int8) >>> row = array([0,2,2,0,1,2]) >>> col = array([0,0,1,2,2,2]) >>> data = array([1,2,3,4,5,6]) >>> csc_matrix( (data,(row,col)), shape=(3,3) ).todense() matrix([[1, 0, 4], [0, 0, 5], [2, 3, 6]]) >>> indptr = array([0,2,3,6]) >>> indices = array([0,2,2,0,1,2]) >>> data = array([1,2,3,4,5,6]) >>> csc_matrix( (data,indices,indptr), shape=(3,3) ).todense() matrix([[1, 0, 4], [0, 0, 5], [2, 3, 6]]) """    def transpose(self, copy=False):        from csr import csr_matrix        M,N = self.shape        return csr_matrix((self.data,self.indices,self.indptr),(N,M),copy=copy)    def __iter__(self):        csr = self.tocsr()        for r in xrange(self.shape[0]):            yield csr[r,:]    def tocsc(self, copy=False):        if copy:            return self.copy()        else:            return self    def tocsr(self):        M,N = self.shape        indptr = np.empty(M + 1, dtype=np.intc)        indices = np.empty(self.nnz, dtype=np.intc)        data = np.empty(self.nnz, dtype=upcast(self.dtype))        csc_tocsr(M, N, \                 self.indptr, self.indices, self.data, \                 indptr, indices, data)        from csr import csr_matrix        A = csr_matrix((data, indices, indptr), shape=self.shape)        A.has_sorted_indices = True        return A    def __getitem__(self, key):        # use CSR to implement fancy indexing        if isinstance(key, tuple):            row = key[0]            col = key[1]            if isintlike(row) or isinstance(row, slice):                return self.T[col,row].T            else:                #[[1,2],??] or [[[1],[2]],??]                if isintlike(col) or isinstance(col,slice):                    return self.T[col,row].T                else:                    row = np.asarray(row, dtype=np.intc)                    col = np.asarray(col, dtype=np.intc)                    if len(row.shape) == 1:                        return self.T[col,row]                    elif len(row.shape) == 2:                        row = row.reshape(-1)                        col = col.reshape(-1,1)                        return self.T[col,row].T                    else:                        raise NotImplementedError('unsupported indexing')            return self.T[col,row].T        elif isintlike(key) or isinstance(key,slice):            return self.T[:,key].T #[i] or [1:2]        else:            return self.T[:,key].T #[[1,2]]    # these functions are used by the parent class (_cs_matrix)    # to remove redudancy between csc_matrix and csr_matrix    def _swap(self,x):        """swap the members of x if this is a column-oriented matrix """        return (x[1],x[0])def isspmatrix_csc(x):    return isinstance(x, csc_matrix)`
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