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Fonction diags - module scipy.sparse

Signature de la fonction diags

def diags(diagonals, offsets=0, shape=None, format=None, dtype=None) 

Description

diags.__doc__

    Construct a sparse matrix from diagonals.

    Parameters
    ----------
    diagonals : sequence of array_like
        Sequence of arrays containing the matrix diagonals,
        corresponding to `offsets`.
    offsets : sequence of int or an int, optional
        Diagonals to set:
          - k = 0  the main diagonal (default)
          - k > 0  the kth upper diagonal
          - k < 0  the kth lower diagonal
    shape : tuple of int, optional
        Shape of the result. If omitted, a square matrix large enough
        to contain the diagonals is returned.
    format : {"dia", "csr", "csc", "lil", ...}, optional
        Matrix format of the result. By default (format=None) an
        appropriate sparse matrix format is returned. This choice is
        subject to change.
    dtype : dtype, optional
        Data type of the matrix.

    See Also
    --------
    spdiags : construct matrix from diagonals

    Notes
    -----
    This function differs from `spdiags` in the way it handles
    off-diagonals.

    The result from `diags` is the sparse equivalent of::

        np.diag(diagonals[0], offsets[0])
        + ...
        + np.diag(diagonals[k], offsets[k])

    Repeated diagonal offsets are disallowed.

    .. versionadded:: 0.11

    Examples
    --------
    >>> from scipy.sparse import diags
    >>> diagonals = [[1, 2, 3, 4], [1, 2, 3], [1, 2]]
    >>> diags(diagonals, [0, -1, 2]).toarray()
    array([[1, 0, 1, 0],
           [1, 2, 0, 2],
           [0, 2, 3, 0],
           [0, 0, 3, 4]])

    Broadcasting of scalars is supported (but shape needs to be
    specified):

    >>> diags([1, -2, 1], [-1, 0, 1], shape=(4, 4)).toarray()
    array([[-2.,  1.,  0.,  0.],
           [ 1., -2.,  1.,  0.],
           [ 0.,  1., -2.,  1.],
           [ 0.,  0.,  1., -2.]])


    If only one diagonal is wanted (as in `numpy.diag`), the following
    works as well:

    >>> diags([1, 2, 3], 1).toarray()
    array([[ 0.,  1.,  0.,  0.],
           [ 0.,  0.,  2.,  0.],
           [ 0.,  0.,  0.,  3.],
           [ 0.,  0.,  0.,  0.]])