dpnp.arange

dpnp.arange(start, /, stop=None, step=1, *, dtype=None, like=None, device=None, usm_type='device', sycl_queue=None)[source]

Returns an array with evenly spaced values within a given interval.

For full documentation refer to numpy.arange.

Parameters:

  • start ({int, real}, optional) -- Start of interval. The interval includes this value. The default start value is 0.

  • stop ({int, real}) -- End of interval. The interval does not include this value, except in some cases where step is not an integer and floating point round-off affects the length of out.

  • step ({int, real}, optional) -- Spacing between values. The default step size is 1. If step is specified as a position argument, start must also be given.

  • dtype ({None, str, dtype object}, optional) -- The desired dtype for the array. If not given, a default dtype will be used that can represent the values (by considering Promotion Type Rule and device capabilities when necessary).

  • 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 dpctl.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:

out -- The 1-D array containing evenly spaced values.

Return type:

dpnp.ndarray

Limitations

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

See also

dpnp.linspace

Evenly spaced numbers with careful handling of endpoints.

Examples

>>> import dpnp as np
>>> np.arange(3)
array([0, 1, 2])
>>> np.arange(3, 7)
array([3, 4, 5, 6])
>>> np.arange(3, 7, 2)
array([3, 5])

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

>>> x = np.arange(3)  # default case
>>> x, x.device, x.usm_type
(array([0, 1, 2]), Device(level_zero:gpu:0), 'device')

>>> y = np.arange(3, device="cpu")
>>> y, y.device, y.usm_type
(array([0, 1, 2]), Device(opencl:cpu:0), 'device')

>>> z = np.arange(3, usm_type="host")
>>> z, z.device, z.usm_type
(array([0, 1, 2]), Device(level_zero:gpu:0), 'host')