Function fill_diagonal

numpy/lib/_index_tricks_impl.py:795–923

Fill the main diagonal of the given array of any dimensionality. For an array `a` with ``a.ndim >= 2``, the diagonal is the list of values ``a[i, ..., i]`` with indices ``i`` all identical. This function modifies the input array in-place without returning a value. Parameters -

(a, val, wrap=False)

@array_function_dispatch(_fill_diagonal_dispatcher)
def fill_diagonal(a, val, wrap=False):
    """Fill the main diagonal of the given array of any dimensionality.

    For an array `a` with ``a.ndim >= 2``, the diagonal is the list of
    values ``a[i, ..., i]`` with indices ``i`` all identical.  This function
    modifies the input array in-place without returning a value.

    Parameters
    ----------
    a : array, at least 2-D.
      Array whose diagonal is to be filled in-place.
    val : scalar or array_like
      Value(s) to write on the diagonal. If `val` is scalar, the value is
      written along the diagonal. If array-like, the flattened `val` is
      written along the diagonal, repeating if necessary to fill all
      diagonal entries.

    wrap : bool
      For tall matrices in NumPy version up to 1.6.2, the
      diagonal "wrapped" after N columns. You can have this behavior
      with this option. This affects only tall matrices.

    See also
    --------
    diag_indices, diag_indices_from

    Notes
    -----
    This functionality can be obtained via `diag_indices`, but internally
    this version uses a much faster implementation that never constructs the
    indices and uses simple slicing.

    Examples
    --------
    >>> import numpy as np
    >>> a = np.zeros((3, 3), int)
    >>> np.fill_diagonal(a, 5)
    >>> a
    array([[5, 0, 0],
           [0, 5, 0],
           [0, 0, 5]])

    The same function can operate on a 4-D array:

    >>> a = np.zeros((3, 3, 3, 3), int)
    >>> np.fill_diagonal(a, 4)

    We only show a few blocks for clarity:

    >>> a[0, 0]
    array([[4, 0, 0],
           [0, 0, 0],
           [0, 0, 0]])
    >>> a[1, 1]
    array([[0, 0, 0],
           [0, 4, 0],
           [0, 0, 0]])
    >>> a[2, 2]