dpnp.ogrid

dpnp.ogrid = <dpnp.dpnp_iface_arraycreation.OGridClass object>

Construct an open multi-dimensional "meshgrid".

For full documentation refer to numpy.ogrid.

Parameters:

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{None, "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 the input is a single slice, returns an array. If the input is multiple slices, returns a tuple of arrays, with only one dimension not equal to 1.

See also

dpnp.mgrid

Work like dpnp.ogrid but returns dense (or fleshed out) mesh grids.

dpnp.meshgrid

Return coordinate matrices from coordinate vectors.

dpnp.r_

Array concatenator.

Examples

>>> import dpnp as np
>>> np.ogrid[0:5, 0:5]
(array([[0],
        [1],
        [2],
        [3],
        [4]]), array([[0, 1, 2, 3, 4]]))

Creating an array on a different device or with a specified usm_type

>>> x = np.ogrid[-1:1:5j] # default case
>>> x, x.device, x.usm_type
(array([-1. , -0.5,  0. ,  0.5,  1. ]), Device(level_zero:gpu:0), 'device')

>>> y = np.ogrid(device="cpu")[-1:1:5j]
>>> y, y.device, y.usm_type
(array([-1. , -0.5,  0. ,  0.5,  1. ]), Device(opencl:cpu:0), 'device')

>>> z = np.ogrid(usm_type="host")[-1:1:5j]
>>> z, z.device, z.usm_type
(array([-1. , -0.5,  0. ,  0.5,  1. ]), Device(level_zero:gpu:0), 'host')