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File Content: `extract.cpython-35.pyc`



DB�Wg������������������@���s����d��Z��d�d�l�m�Z�m�Z�m�Z�d�Z�d�d�d�g�Z�d�d�l�m�Z�d	�d����Z	�d�d
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�S)z.Functions to extract parts of sparse matrices
�����)�division�print_function�absolute_importzrestructuredtext en�find�tril�triu����)�
coo_matrixc�������������C���sM���t��|��d�d��}��|��j����|��j�d�k�}�|��j�|�|��j�|�|��j�|�f�S)aJ��Return the indices and values of the nonzero elements of a matrix

    Parameters
    ----------
    A : dense or sparse matrix
        Matrix whose nonzero elements are desired.

    Returns
    -------
    (I,J,V) : tuple of arrays
        I,J, and V contain the row indices, column indices, and values
        of the nonzero matrix entries.


    Examples
    --------
    >>> from scipy.sparse import csr_matrix, find
    >>> A = csr_matrix([[7.0, 8.0, 0],[0, 0, 9.0]])
    >>> find(A)
    (array([0, 0, 1], dtype=int32), array([0, 1, 2], dtype=int32), array([ 7.,  8.,  9.]))

    �copyTr���)r	���Zsum_duplicates�data�row�col)�AZnz_mask��r����/extract.pyr������s����
Nc�������������C���s>���t��|��d�d��}��|��j�|�|��j�k�}�t�|��|���j�|���S)a��Return the lower triangular portion of a matrix in sparse format

    Returns the elements on or below the k-th diagonal of the matrix A.
        - k = 0 corresponds to the main diagonal
        - k > 0 is above the main diagonal
        - k < 0 is below the main diagonal

    Parameters
    ----------
    A : dense or sparse matrix
        Matrix whose lower trianglar portion is desired.
    k : integer : optional
        The top-most diagonal of the lower triangle.
    format : string
        Sparse format of the result, e.g. format="csr", etc.

    Returns
    -------
    L : sparse matrix
        Lower triangular portion of A in sparse format.

    See Also
    --------
    triu : upper triangle in sparse format

    Examples
    --------
    >>> from scipy.sparse import csr_matrix, tril
    >>> A = csr_matrix([[1, 2, 0, 0, 3], [4, 5, 0, 6, 7], [0, 0, 8, 9, 0]],
    ...                dtype='int32')
    >>> A.toarray()
    array([[1, 2, 0, 0, 3],
           [4, 5, 0, 6, 7],
           [0, 0, 8, 9, 0]])
    >>> tril(A).toarray()
    array([[1, 0, 0, 0, 0],
           [4, 5, 0, 0, 0],
           [0, 0, 8, 0, 0]])
    >>> tril(A).nnz
    4
    >>> tril(A, k=1).toarray()
    array([[1, 2, 0, 0, 0],
           [4, 5, 0, 0, 0],
           [0, 0, 8, 9, 0]])
    >>> tril(A, k=-1).toarray()
    array([[0, 0, 0, 0, 0],
           [4, 0, 0, 0, 0],
           [0, 0, 0, 0, 0]])
    >>> tril(A, format='csc')
    <3x5 sparse matrix of type '<type 'numpy.int32'>'
            with 4 stored elements in Compressed Sparse Column format>

    r
���F)r	���r���r
����_masked_coo�asformat)r����k�format�maskr���r���r���r���-���s����8c�������������C���s>���t��|��d�d��}��|��j�|�|��j�k�}�t�|��|���j�|���S)a��Return the upper triangular portion of a matrix in sparse format

    Returns the elements on or above the k-th diagonal of the matrix A.
        - k = 0 corresponds to the main diagonal
        - k > 0 is above the main diagonal
        - k < 0 is below the main diagonal

    Parameters
    ----------
    A : dense or sparse matrix
        Matrix whose upper trianglar portion is desired.
    k : integer : optional
        The bottom-most diagonal of the upper triangle.
    format : string
        Sparse format of the result, e.g. format="csr", etc.

    Returns
    -------
    L : sparse matrix
        Upper triangular portion of A in sparse format.

    See Also
    --------
    tril : lower triangle in sparse format

    Examples
    --------
    >>> from scipy.sparse import csr_matrix, triu
    >>> A = csr_matrix([[1, 2, 0, 0, 3], [4, 5, 0, 6, 7], [0, 0, 8, 9, 0]],
    ...                dtype='int32')
    >>> A.toarray()
    array([[1, 2, 0, 0, 3],
           [4, 5, 0, 6, 7],
           [0, 0, 8, 9, 0]])
    >>> triu(A).toarray()
    array([[1, 2, 0, 0, 3],
           [0, 5, 0, 6, 7],
           [0, 0, 8, 9, 0]])
    >>> triu(A).nnz
    8
    >>> triu(A, k=1).toarray()
    array([[0, 2, 0, 0, 3],
           [0, 0, 0, 6, 7],
           [0, 0, 0, 9, 0]])
    >>> triu(A, k=-1).toarray()
    array([[1, 2, 0, 0, 3],
           [4, 5, 0, 6, 7],
           [0, 0, 8, 9, 0]])
    >>> triu(A, format='csc')
    <3x5 sparse matrix of type '<type 'numpy.int32'>'
            with 8 stored elements in Compressed Sparse Column format>

    r
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���r���r���r���r���r�������s����


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Name	Size	Permission
__init__.cpython-35.opt-1.pyc	6603 bytes	0644
__init__.cpython-35.pyc	6603 bytes	0644
base.cpython-35.opt-1.pyc	34287 bytes	0644
base.cpython-35.pyc	34287 bytes	0644
bsr.cpython-35.opt-1.pyc	18341 bytes	0644
bsr.cpython-35.pyc	18341 bytes	0644
compressed.cpython-35.opt-1.pyc	30815 bytes	0644
compressed.cpython-35.pyc	30815 bytes	0644
construct.cpython-35.opt-1.pyc	22666 bytes	0644
construct.cpython-35.pyc	22666 bytes	0644
coo.cpython-35.opt-1.pyc	15485 bytes	0644
coo.cpython-35.pyc	15485 bytes	0644
csc.cpython-35.opt-1.pyc	6762 bytes	0644
csc.cpython-35.pyc	6762 bytes	0644
csr.cpython-35.opt-1.pyc	13129 bytes	0644
csr.cpython-35.pyc	13173 bytes	0644
data.cpython-35.opt-1.pyc	8454 bytes	0644
data.cpython-35.pyc	8454 bytes	0644
dia.cpython-35.opt-1.pyc	10965 bytes	0644
dia.cpython-35.pyc	10965 bytes	0644
dok.cpython-35.opt-1.pyc	15532 bytes	0644
dok.cpython-35.pyc	15567 bytes	0644
extract.cpython-35.opt-1.pyc	4997 bytes	0644
extract.cpython-35.pyc	4997 bytes	0644
generate_sparsetools.cpython-35.opt-1.pyc	10483 bytes	0644
generate_sparsetools.cpython-35.pyc	10483 bytes	0644
lil.cpython-35.opt-1.pyc	15956 bytes	0644
lil.cpython-35.pyc	15956 bytes	0644
setup.cpython-35.opt-1.pyc	1957 bytes	0644
setup.cpython-35.pyc	1957 bytes	0644
sparsetools.cpython-35.opt-1.pyc	672 bytes	0644
sparsetools.cpython-35.pyc	672 bytes	0644
spfuncs.cpython-35.opt-1.pyc	2264 bytes	0644
spfuncs.cpython-35.pyc	2264 bytes	0644
sputils.cpython-35.opt-1.pyc	10713 bytes	0644
sputils.cpython-35.pyc	10713 bytes	0644

File Content: extract.cpython-35.pyc

File Content: `extract.cpython-35.pyc`