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# numpy.ma.indices

`numpy.ma.``indices`(dimensions, dtype=<type 'int'>)

  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.
  Returns : | grid : ndarray The array of grid indices, `grid.shape = (len(dimensions),) + tuple(dimensions)`.

  See also

  `mgrid`, `meshgrid`

  Notes

  The output shape 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]`.