dpnp.fromiter¶

dpnp.fromiter(iter, dtype, count=-1, *, like=None, device=None, usm_type='device', sycl_queue=None)[source]¶

Create a new 1-dimensional array from an iterable object.

For full documentation refer to numpy.fromiter.

Parameters:

iteriterable object

An iterable object providing data for the array.

dtype{None, str, dtype object}

The data-type of the returned array.

countint, optional

The number of items to read from iterable. The default is -1, which means all data is read.

Default: -1.

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 output array.

Limitations

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

See also

dpnp.frombuffer: Construct array from the buffer data.
dpnp.fromfile: Construct array from data in a text or binary file.
dpnp.fromstring: Construct array from the text data in a string.

Notes

This uses numpy.fromiter and coerces the result to a DPNP array.

Examples

>>> import dpnp as np
>>> iterable = (a * a for a in range(5))
>>> np.fromiter(iterable, float)
array([  0.,   1.,   4.,   9.,  16.])

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

>>> x = np.fromiter(iterable, np.int32) # default case
>>> x.device, x.usm_type
(Device(level_zero:gpu:0), 'device')

>>> y = np.fromiter(iterable, np.int32, device='cpu')
>>> y.device, y.usm_type
(Device(opencl:cpu:0), 'device')

>>> z = np.fromiter(iterable, np.int32, usm_type="host")
>>> z.device, z.usm_type
(Device(level_zero:gpu:0), 'host')