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scipy.linalg.

# block_diag

scipy.linalg.block_diag(*arrs)

  Create a block diagonal array from provided arrays.

  For example, given 2-D inputs A, B and C, the output will have these arrays arranged on the diagonal:

      [[A, 0, 0],
       [0, B, 0],
       [0, 0, C]]

  The documentation is written assuming array arguments are of specified “core” shapes. However, array argument(s) of this function may have additional “batch” dimensions prepended to the core shape. In this case, the array is treated as a batch of lower-dimensional slices; see Batched Linear Operations for details.

  Parameters:
    A, B, C, …array_like

      Input arrays. A 1-D array or array_like sequence of length `n` is treated as a 2-D array with shape `(1, n)`.

  Returns:
    D ndarray

      Array with A, B, C, … on the diagonal of the last two dimensions. D has the same dtype as the result type of the inputs.

  Notes

  If all the input arrays are square, the output is known as a block diagonal matrix.

  Empty sequences (i.e., array-likes of zero size) will not be ignored. Noteworthy, both `[]` and `[[]]` are treated as matrices with shape `(1,0)`.

  Array API Standard Support

  `block_diag` has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable `SCIPY_ARRAY_API=1` and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

  Library | CPU | GPU
  --- | --- | ---
  NumPy | ✅ | n/a
  CuPy | n/a | ✅
  PyTorch | ✅ | ✅
  JAX | ⚠️ no JIT | ⚠️ no JIT
  Dask | ⚠️ computes graph | n/a

  > See Support for the array API standard for more information.

  Examples

      >>> import numpy as np
      >>> from scipy.linalg import block_diag
      >>> A = [[1, 0],
      ...      [0, 1]]
      >>> B = [[3, 4, 5],
      ...      [6, 7, 8]]
      >>> C = [[7]]
      >>> P = np.zeros((2, 0), dtype='int32')
      >>> block_diag(A, B, C)
      array([[1, 0, 0, 0, 0, 0],
             [0, 1, 0, 0, 0, 0],
             [0, 0, 3, 4, 5, 0],
             [0, 0, 6, 7, 8, 0],
             [0, 0, 0, 0, 0, 7]])
      >>> block_diag(A, P, B, C)
      array([[1, 0, 0, 0, 0, 0],
             [0, 1, 0, 0, 0, 0],
             [0, 0, 0, 0, 0, 0],
             [0, 0, 0, 0, 0, 0],
             [0, 0, 3, 4, 5, 0],
             [0, 0, 6, 7, 8, 0],
             [0, 0, 0, 0, 0, 7]])
      >>> block_diag(1.0, [2, 3], [[4, 5], [6, 7]])
      array([[ 1.,  0.,  0.,  0.,  0.],
             [ 0.,  2.,  3.,  0.,  0.],
             [ 0.,  0.,  0.,  4.,  5.],
             [ 0.,  0.,  0.,  6.,  7.]])