- xorbits.numpy.indices(dimensions, dtype=<class 'int'>, chunk_size=None)#
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.
dimensions (sequence of ints) – The shape of the grid.
dtype (dtype, optional) – Data type of the result.
sparse (boolean, optional (Not supported yet)) –
Return a sparse representation of the grid instead of a dense representation. Default is False.
New in version 1.17(numpy).
- 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
- Return type
one ndarray or tuple of ndarrays
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).
grid[k]contains the N-D array of indices along the
grid[k, i0, i1, ..., iN-1] = ik
>>> grid = np.indices((2, 3)) >>> grid.shape (2, 2, 3) >>> grid # row indices array([[0, 0, 0], [1, 1, 1]]) >>> grid # 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
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([, ]) >>> j # column indices array([[0, 1, 2]])
- chunk_sizeint or tuple of int or tuple of ints, optional
Desired chunk size on each dimension
This docstring was copied from numpy.