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# 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=∑i=0 M−1(−1)i a i cos⁡(2 π i n M−1)w_n = \sum^{M-1}_{i=0} (-1)^i a_i \cos{ \left( \frac{2 \pi i n}{M - 1}\right)} w n​=i=0∑M−1​(−1)i a i​cos(M−1 2 πin​)

  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])

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