tensorplay.signal.windows.hamming

tensorplay.signal.windows.hamming(M: int, *, sym: bool = True, dtype: dtype | None = None, layout: Layout = tensorplay.strided, device: device | None = None, requires_grad: bool = False) → Tensor[source]

Computes the Hamming window.

The samples are

$$w_n = \alpha - \beta\ \cos \left( \frac{2 \pi n}{M - 1} \right)$$

with $\alpha = 0.54$ and $\beta = 0.46$.

The window is scaled so that its largest value is 1. The value 1 itself does not occur when M is even and sym is True.

Parameters:

M (int) – number of points of the returned window.

Keyword Arguments:

• sym (bool, optional) – if False, returns a periodic window, which is the usual choice for spectral analysis. If True, returns a symmetric window, which is the usual choice for filter design. Default: True.

• dtype (tensorplay.dtype, optional) – the desired data type of the returned tensor. Default: if None, uses the global default (see tensorplay.set_default_dtype()).

• layout (tensorplay.Layout, optional) – the desired layout of the returned tensor. Default: tensorplay.strided.

• device (tensorplay.device, optional) – the desired device of the returned tensor. Default: if None, uses the current default tensor device (see tensorplay.set_default_device()).

• requires_grad (bool, optional) – whether autograd should record operations on the returned tensor. Default: False.

Examples:

>>> # Symmetric Hamming window.
>>> tensorplay.signal.windows.hamming(10)
tensor([0.08, 0.1876, 0.4601, 0.77, 0.9723, 0.9723, 0.77, 0.4601, 0.1876, 0.08])

>>> # Periodic Hamming window.
>>> tensorplay.signal.windows.hamming(10, sym=False)
tensor([0.08, 0.1679, 0.3979, 0.6821, 0.9121, 1., 0.9121, 0.6821, 0.3979, 0.1679])