dpnp.mgrid

dpnp.mgrid = <dpnp.dpnp_iface_arraycreation.MGridClass object>

Construct a dense multi-dimensional "meshgrid".

For full documentation refer to numpy.mgrid.

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:

outdpnp.ndarray

A single array, containing a set of arrays all of the same dimensions, stacked along the first axis.

See also

dpnp.ogrid

Work like dpnp.mgrid but returns open (not fleshed out) mesh grids.

dpnp.meshgrid

Return coordinate matrices from coordinate vectors.

dpnp.r_

Array concatenator.

Examples

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

>>> np.mgrid[0:4].shape
(4,)
>>> np.mgrid[0:4, 0:5].shape
(2, 4, 5)
>>> np.mgrid[0:4, 0:5, 0:6].shape
(3, 4, 5, 6)

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

>>> x = np.mgrid[-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.mgrid(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.mgrid(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')