# numpy.indices

numpy.indices(dimensions, dtype=<class 'int'>, sparse=False)

  Return an array representing the indices of a grid.

  Compute an array where the subarrays contain index values 0, 1, … varying only along the corresponding axis.

  Parameters
    dimensions sequence of ints

      The shape of the grid.

    dtype dtype, optional

      Data type of the result.

    sparse boolean, optional

      Return a sparse representation of the grid instead of a dense representation. Default is False.

      New in version 1.17.

  Returns
    grid one ndarray or tuple of ndarrays
      If sparse is False:

        Returns one array of grid indices, `grid.shape = (len(dimensions),) + tuple(dimensions)`.

      If sparse is True:

        Returns a tuple of arrays, with `grid[i].shape = (1, ..., 1, dimensions[i], 1, ..., 1)` with dimensions[i] in the ith place

  See also

  `mgrid`, `ogrid`, `meshgrid`

  Notes

  The output shape in the dense case is obtained by prepending the number of dimensions in front of the tuple of dimensions, i.e. if dimensions is a tuple `(r0, ..., rN-1)` of length `N`, the output shape is `(N, r0, ..., rN-1)`.

  The subarrays `grid[k]` contains the N-D array of indices along the `k-th` axis. Explicitly:

      grid[k, i0, i1, ..., iN-1] = ik

  Examples

      >>> grid = np.indices((2, 3))
      >>> grid.shape
      (2, 2, 3)
      >>> grid[0]        # row indices
      array([[0, 0, 0],
             [1, 1, 1]])
      >>> grid[1]        # column indices
      array([[0, 1, 2],
             [0, 1, 2]])

  The indices can be used as an index into an array.

      >>> x = np.arange(20).reshape(5, 4)
      >>> row, col = np.indices((2, 3))
      >>> x[row, col]
      array([[0, 1, 2],
             [4, 5, 6]])

  Note that it would be more straightforward in the above example to extract the required elements directly with `x[:2, :3]`.

  If sparse is set to true, the grid will be returned in a sparse representation.

      >>> i, j = np.indices((2, 3), sparse=True)
      >>> i.shape
      (2, 1)
      >>> j.shape
      (1, 3)
      >>> i        # row indices
      array([[0],
             [1]])
      >>> j        # column indices
      array([[0, 1, 2]])

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