dpnp.hanning¶

dpnp.hanning(M, *, device=None, usm_type=None, sycl_queue=None)[source]¶

Return the Hanning window.

The Hanning window is a taper formed by using a weighted cosine.

For full documentation refer to numpy.hanning.

Parameters:

Mint

Number of points in the output window. If zero or less, an empty array is returned.

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: None.

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 of shape (M,): The window, with the maximum value normalized to one (the value one appears only if the number of samples is odd).

See also

dpnp.bartlett: Return the Bartlett window.
dpnp.blackman: Return the Blackman window.
dpnp.hamming: Return the Hamming window.
dpnp.kaiser: Return the Kaiser window.

Notes

The Hanning window is defined as

\[w(n) = 0.5 - 0.5\cos\left(\frac{2\pi{n}}{M-1}\right) \qquad 0 \leq n \leq M-1\]

Examples

>>> import dpnp as np
>>> np.hanning(12)
array([0.        , 0.07937323, 0.29229249, 0.57115742, 0.82743037,
       0.97974649, 0.97974649, 0.82743037, 0.57115742, 0.29229249,
       0.07937323, 0.        ])

Creating the output array on a different device or with a specified usm_type:

>>> x = np.hanning(4) # default case
>>> x, x.device, x.usm_type
(array([0.  , 0.75, 0.75, 0.  ]), Device(level_zero:gpu:0), 'device')

>>> y = np.hanning(4, device="cpu")
>>> y, y.device, y.usm_type
(array([0.  , 0.75, 0.75, 0.  ]), Device(opencl:cpu:0), 'device')

>>> z = np.hanning(4, usm_type="host")
>>> z, z.device, z.usm_type
(array([0.  , 0.75, 0.75, 0.  ]), Device(level_zero:gpu:0), 'host')