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torch.signal.windows.nuttall
torch.signal.windows.nuttall(M, *, sym=True, dtype=None, layout=torch.strided, device=None, requires_grad=False)
[source]-
Computes the minimum 4-term Blackman-Harris window according to Nuttall.
where
z_n = 2 π n/ M
.The window is normalized to 1 (maximum value is 1). However, the 1 doesn’t appear if
M
is even andsym
isTrue
.- Parameters
-
M (int) – the length of the window. In other words, the number of points of the returned window.
- Keyword Arguments
-
- sym (bool, optional) – If
False
, returns a periodic window suitable for use in spectral analysis. IfTrue
, returns a symmetric window suitable for use in filter design. Default:True
. - dtype (
torch.dtype
, optional) – the desired data type of returned tensor. Default: ifNone
, uses a global default (seetorch.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: ifNone
, uses the current device for the default tensor type (seetorch.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
.
- sym (bool, optional) – If
- Return type
References:
- A. Nuttall, “Some windows with very good sidelobe behavior,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 29, no. 1, pp. 84-91, Feb 1981. https://doi.org/10.1109/TASSP.1981.1163506 - Heinzel G. et al., “Spectrum and spectral density estimation by the Discrete Fourier transform (DFT), including a comprehensive list of window functions and some new flat-top windows”, February 15, 2002 https://holometer.fnal.gov/GH_FFT.pdf
Examples:
>>> # Generates a symmetric Nutall window. >>> torch.signal.windows.general_hamming(5, sym=True) tensor([3.6280e-04, 2.2698e-01, 1.0000e+00, 2.2698e-01, 3.6280e-04]) >>> # Generates a periodic Nuttall window. >>> torch.signal.windows.general_hamming(5, sym=False) tensor([3.6280e-04, 1.1052e-01, 7.9826e-01, 7.9826e-01, 1.1052e-01])
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