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:

  • iter (iterable object) -- An iterable object providing data for the array.

  • dtype (data-type) -- The data-type of the returned array.

  • count (int, optional) -- The number of items to read from iterable. The default is -1, which means all data is read.

  • device ({None, string, SyclDevice, SyclQueue}, optional) -- An array API concept of device where the output array is created. The device can be None (the default), an OneAPI filter selector string, an instance of dpctl.SyclDevice corresponding to a non-partitioned SYCL device, an instance of dpctl.SyclQueue, or a Device object returned by dpnp.dpnp_array.dpnp_array.device property.

  • usm_type ({None, "device", "shared", "host"}, optional) -- The type of SYCL USM allocation for the output array. Default is "device".

  • sycl_queue ({None, SyclQueue}, optional) -- A SYCL queue to use for output array allocation and copying.

Returns:

out -- The output array.

Return type:

dpnp.ndarray

Limitations

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

Notes

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

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.

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')