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scipy.sparse.

# spdiags

scipy.sparse.spdiags(data, diags, m=None, n=None, format=None)

  Return a sparse matrix from diagonals.

  Warning

  This function returns a sparse matrix – not a sparse array. You are encouraged to use `dia_array` to take advantage of the sparse array functionality. (See Notes below.)

  Parameters:
    dataarray_like

      Matrix diagonals stored row-wise

    diagssequence of int or an int

      Diagonals to set:

      - k = 0 the main diagonal

      - k > 0 the kth upper diagonal

      - k < 0 the kth lower diagonal

    m, nint, tuple, optional

      Shape of the result. If n is None and m is a given tuple, the shape is this tuple. If omitted, the matrix is square and its shape is `len(data[0])`.

    formatstr, optional

      Format of the result. By default (format=None) an appropriate sparse matrix format is returned. This choice is subject to change.

  Returns:
    new_matrixsparse matrix

      `dia_matrix` format with values in `data` on diagonals from `diags`.

  See also

  `diags_array`

    more convenient form of this function

  `diags`

    matrix version of diags_array

  `dia_matrix`

    the sparse DIAgonal format.

  Notes

  This function can be replaced by an equivalent call to `dia_matrix` as:

      dia_matrix((data, diags), shape=(m, n)).asformat(format)

  Examples

      >>> import numpy as np
      >>> from scipy.sparse import spdiags
      >>> data = np.array([[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]])
      >>> diags = np.array([0, -1, 2])
      >>> spdiags(data, diags, 4, 4).toarray()
      array([[1, 0, 3, 0],
             [1, 2, 0, 4],
             [0, 2, 3, 0],
             [0, 0, 3, 4]])