dpnp.fromfunction¶

dpnp.fromfunction(function, shape, *, dtype=<class 'float'>, like=None, device=None, usm_type='device', sycl_queue=None, **kwargs)[source]¶

Construct an array by executing a function over each coordinate.

The resulting array therefore has a value fn(x, y, z) at coordinate (x, y, z).

For full documentation refer to numpy.fromfunction.

Parameters:

functioncallable

The function is called with N parameters, where N is the rank of shape. Each parameter represents the coordinates of the array varying along a specific axis. For example, if shape were (2, 2), then the parameters would be array([[0, 0], [1, 1]]) and array([[0, 1], [0, 1]]).

shape(N,) tuple of ints

Shape of the output array, which also determines the shape of the coordinate arrays passed to function.

dtype{None, str, dtype object}, optional

Data-type of the coordinate arrays passed to function. If None, uses a default floating point data type for the device on which the returned array is allocated.

Default: float.

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: The result of the call to function is passed back directly. Therefore the shape of fromfunction is completely determined by function.

Limitations

Parameter like is supported only with default value None. Otherwise, the function raises NotImplementedError exception.

See also

dpnp.indices: Return an array representing the indices of a grid.
dpnp.meshgrid: Return coordinate matrices from coordinate vectors.

Notes

This uses numpy.fromfunction and coerces the result to a DPNP array. Keywords other than dtype and like are passed to function.

Examples

>>> import dpnp as np
>>> np.fromfunction(lambda i, j: i, (2, 2), dtype=float)
array([[0., 0.],
       [1., 1.]])

>>> np.fromfunction(lambda i, j: j, (2, 2), dtype=float)
array([[0., 1.],
       [0., 1.]])

>>> np.fromfunction(lambda i, j: i == j, (3, 3), dtype=int)
array([[ True, False, False],
       [False,  True, False],
       [False, False,  True]])

>>> np.fromfunction(lambda i, j: i + j, (3, 3), dtype=int)
array([[0, 1, 2],
       [1, 2, 3],
       [2, 3, 4]])

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

>>> x = np.fromfunction(lambda i, j: i - j, (3, 3)) # default case
>>> x.device, x.usm_type
(Device(level_zero:gpu:0), 'device')

>>> y = np.fromfunction(lambda i, j: i - j, (3, 3), device='cpu')
>>> y.device, y.usm_type
(Device(opencl:cpu:0), 'device')

>>> z = np.fromfunction(lambda i, j: i - j, (3, 3), usm_type="host")
>>> z.device, z.usm_type
(Device(level_zero:gpu:0), 'host')