dpnp.indices¶

dpnp.indices(dimensions, dtype=<class 'int'>, sparse=False, device=None, usm_type='device', sycl_queue=None)[source]¶

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.

For full documentation refer to numpy.indices.

Parameters:

dimensionssequence of ints

The shape of the grid.

dtype{None, str, dtype object}, optional

Data type of the result.

Default: int.

sparse{None, boolean}, optional

Return a sparse representation of the grid instead of a dense representation.

Default: False.

device{None, string, SyclDevice, SyclQueue, Device}, optional

An array API concept of device where the output array is created. device can be None, a oneAPI filter selector string, an instance of dpctl.SyclDevice corresponding to a non-partitioned SYCL device, an instance of dpctl.SyclQueue, or a dpnp.tensor.Device object returned by dpnp.ndarray.device.

Default: None.

usm_type{"device", "shared", "host"}, optional

The type of SYCL USM allocation for the output array.

Default: "device".

sycl_queue{None, SyclQueue}, optional

A SYCL queue to use for output array allocation and copying. The sycl_queue can be passed as None (the default), which means to get the SYCL queue from device keyword if present or to use a default queue.

Default: None.

Returns:

outone dpnp.ndarray or tuple of dpnp.ndarray

If sparse is False: Returns one array of grid indices with 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 i-th place.

See also

dpnp.mgrid: Return a dense multi-dimensional “meshgrid”.
dpnp.ogrid: Return an open multi-dimensional “meshgrid”.
dpnp.meshgrid: Return a tuple of coordinate matrices from coordinate vectors.

Examples

>>> import dpnp as np
>>> grid = np.indices((2, 3))
>>> grid.shape
(2, 2, 3)
>>> grid[0]
array([[0, 0, 0],
       [1, 1, 1]])
>>> grid[1]
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
array([[0],
       [1]])
>>> j
array([[0, 1, 2]])