numpy.fill_diagonal#
- numpy.fill_diagonal(a, val, wrap=False)[source]#
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 valuesa[i, ..., i]
with indicesi
all identical. This function modifies the input array in-place without returning a value.- Parameters:
- aarray, at least 2-D.
Array whose diagonal is to be filled in-place.
- valscalar 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.
- wrapbool
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
Notes
New in version 1.4.0.
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
>>> 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] array([[0, 0, 0], [0, 0, 0], [0, 0, 4]])
The wrap option affects only tall matrices:
>>> # tall matrices no wrap >>> a = np.zeros((5, 3), int) >>> np.fill_diagonal(a, 4) >>> a array([[4, 0, 0], [0, 4, 0], [0, 0, 4], [0, 0, 0], [0, 0, 0]])
>>> # tall matrices wrap >>> a = np.zeros((5, 3), int) >>> np.fill_diagonal(a, 4, wrap=True) >>> a array([[4, 0, 0], [0, 4, 0], [0, 0, 4], [0, 0, 0], [4, 0, 0]])
>>> # wide matrices >>> a = np.zeros((3, 5), int) >>> np.fill_diagonal(a, 4, wrap=True) >>> a array([[4, 0, 0, 0, 0], [0, 4, 0, 0, 0], [0, 0, 4, 0, 0]])
The anti-diagonal can be filled by reversing the order of elements using either
numpy.flipud
ornumpy.fliplr
.>>> a = np.zeros((3, 3), int); >>> np.fill_diagonal(np.fliplr(a), [1,2,3]) # Horizontal flip >>> a array([[0, 0, 1], [0, 2, 0], [3, 0, 0]]) >>> np.fill_diagonal(np.flipud(a), [1,2,3]) # Vertical flip >>> a array([[0, 0, 3], [0, 2, 0], [1, 0, 0]])
Note that the order in which the diagonal is filled varies depending on the flip function.