torch.signal.windows.general_cosine

torch.signal.windows.general_cosine(M, *, a, sym=True, dtype=None, layout=torch.strided, device=None, requires_grad=False)

Computes the general cosine window.

The general cosine window is defined as follows:

$$w_n = \sum^{M-1}_{i=0} (-1)^i a_i \cos{ \left( \frac{2 \pi i n}{M - 1}\right)}$$

The window is normalized to 1 (maximum value is 1). However, the 1 doesn’t appear if M is even and sym is True.

Parameters:

M (int) – the length of the window. In other words, the number of points of the returned window.

Keyword Arguments:

• a (Iterable) – the coefficients associated to each of the cosine functions.

• sym (bool, optional) – If False, returns a periodic window suitable for use in spectral analysis. If True, returns a symmetric window suitable for use in filter design. Default: True.

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

• layout (torch.layout, optional) – the desired layout of returned Tensor. Default: torch.strided.

• device (torch.device, optional) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see torch.set_default_tensor_type()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.

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

Return type:

Tensor

Examples:

>>> # Generates a symmetric general cosine window with 3 coefficients.
>>> torch.signal.windows.general_cosine(10, a=[0.46, 0.23, 0.31], sym=True)
tensor([0.5400, 0.3376, 0.1288, 0.4200, 0.9136, 0.9136, 0.4200, 0.1288, 0.3376, 0.5400])

>>> # Generates a periodic general cosine window wit 2 coefficients.
>>> torch.signal.windows.general_cosine(10, a=[0.5, 1 - 0.5], sym=False)
tensor([0.0000, 0.0955, 0.3455, 0.6545, 0.9045, 1.0000, 0.9045, 0.6545, 0.3455, 0.0955])