806 lines
23 KiB
Python
806 lines
23 KiB
Python
from typing import Optional, Iterable
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import torch
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from math import sqrt
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from torch import Tensor
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from torch._torch_docs import factory_common_args, parse_kwargs, merge_dicts
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__all__ = [
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'bartlett',
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'blackman',
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'cosine',
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'exponential',
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'gaussian',
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'general_cosine',
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'general_hamming',
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'hamming',
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'hann',
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'kaiser',
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'nuttall',
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]
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window_common_args = merge_dicts(
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parse_kwargs(
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"""
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M (int): the length of the window.
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In other words, the number of points of the returned window.
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sym (bool, optional): If `False`, returns a periodic window suitable for use in spectral analysis.
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If `True`, returns a symmetric window suitable for use in filter design. Default: `True`.
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"""
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),
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factory_common_args,
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{
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"normalization": "The window is normalized to 1 (maximum value is 1). However, the 1 doesn't appear if "
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":attr:`M` is even and :attr:`sym` is `True`.",
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}
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)
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def _add_docstr(*args):
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r"""Adds docstrings to a given decorated function.
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Specially useful when then docstrings needs string interpolation, e.g., with
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str.format().
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REMARK: Do not use this function if the docstring doesn't need string
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interpolation, just write a conventional docstring.
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Args:
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args (str):
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"""
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def decorator(o):
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o.__doc__ = "".join(args)
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return o
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return decorator
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def _window_function_checks(function_name: str, M: int, dtype: torch.dtype, layout: torch.layout) -> None:
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r"""Performs common checks for all the defined windows.
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This function should be called before computing any window.
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Args:
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function_name (str): name of the window function.
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M (int): length of the window.
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dtype (:class:`torch.dtype`): the desired data type of returned tensor.
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layout (:class:`torch.layout`): the desired layout of returned tensor.
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"""
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if M < 0:
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raise ValueError(f'{function_name} requires non-negative window length, got M={M}')
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if layout is not torch.strided:
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raise ValueError(f'{function_name} is implemented for strided tensors only, got: {layout}')
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if dtype not in [torch.float32, torch.float64]:
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raise ValueError(f'{function_name} expects float32 or float64 dtypes, got: {dtype}')
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@_add_docstr(
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r"""
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Computes a window with an exponential waveform.
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Also known as Poisson window.
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The exponential window is defined as follows:
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.. math::
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w_n = \exp{\left(-\frac{|n - c|}{\tau}\right)}
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where `c` is the ``center`` of the window.
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""",
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r"""
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{normalization}
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Args:
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{M}
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Keyword args:
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center (float, optional): where the center of the window will be located.
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Default: `M / 2` if `sym` is `False`, else `(M - 1) / 2`.
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tau (float, optional): the decay value.
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Tau is generally associated with a percentage, that means, that the value should
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vary within the interval (0, 100]. If tau is 100, it is considered the uniform window.
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Default: 1.0.
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{sym}
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{dtype}
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{layout}
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{device}
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{requires_grad}
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Examples::
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>>> # Generates a symmetric exponential window of size 10 and with a decay value of 1.0.
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>>> # The center will be at (M - 1) / 2, where M is 10.
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>>> torch.signal.windows.exponential(10)
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tensor([0.0111, 0.0302, 0.0821, 0.2231, 0.6065, 0.6065, 0.2231, 0.0821, 0.0302, 0.0111])
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>>> # Generates a periodic exponential window and decay factor equal to .5
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>>> torch.signal.windows.exponential(10, sym=False,tau=.5)
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tensor([4.5400e-05, 3.3546e-04, 2.4788e-03, 1.8316e-02, 1.3534e-01, 1.0000e+00, 1.3534e-01, 1.8316e-02, 2.4788e-03, 3.3546e-04])
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""".format(
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**window_common_args
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),
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)
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def exponential(
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M: int,
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*,
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center: Optional[float] = None,
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tau: float = 1.0,
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sym: bool = True,
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dtype: Optional[torch.dtype] = None,
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layout: torch.layout = torch.strided,
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device: Optional[torch.device] = None,
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requires_grad: bool = False
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) -> Tensor:
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if dtype is None:
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dtype = torch.get_default_dtype()
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_window_function_checks('exponential', M, dtype, layout)
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if tau <= 0:
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raise ValueError(f'Tau must be positive, got: {tau} instead.')
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if sym and center is not None:
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raise ValueError('Center must be None for symmetric windows')
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if M == 0:
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return torch.empty((0,), dtype=dtype, layout=layout, device=device, requires_grad=requires_grad)
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if center is None:
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center = (M if not sym and M > 1 else M - 1) / 2.0
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constant = 1 / tau
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k = torch.linspace(start=-center * constant,
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end=(-center + (M - 1)) * constant,
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steps=M,
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dtype=dtype,
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layout=layout,
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device=device,
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requires_grad=requires_grad)
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return torch.exp(-torch.abs(k))
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@_add_docstr(
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r"""
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Computes a window with a simple cosine waveform, following the same implementation as SciPy.
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This window is also known as the sine window.
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The cosine window is defined as follows:
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.. math::
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w_n = \sin\left(\frac{\pi (n + 0.5)}{M}\right)
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This formula differs from the typical cosine window formula by incorporating a 0.5 term in the numerator,
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which shifts the sample positions. This adjustment results in a window that starts and ends with non-zero values.
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""",
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r"""
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{normalization}
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Args:
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{M}
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Keyword args:
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{sym}
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{dtype}
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{layout}
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{device}
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{requires_grad}
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Examples::
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>>> # Generates a symmetric cosine window.
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>>> torch.signal.windows.cosine(10)
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tensor([0.1564, 0.4540, 0.7071, 0.8910, 0.9877, 0.9877, 0.8910, 0.7071, 0.4540, 0.1564])
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>>> # Generates a periodic cosine window.
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>>> torch.signal.windows.cosine(10, sym=False)
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tensor([0.1423, 0.4154, 0.6549, 0.8413, 0.9595, 1.0000, 0.9595, 0.8413, 0.6549, 0.4154])
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""".format(
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**window_common_args,
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),
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)
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def cosine(
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M: int,
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*,
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sym: bool = True,
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dtype: Optional[torch.dtype] = None,
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layout: torch.layout = torch.strided,
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device: Optional[torch.device] = None,
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requires_grad: bool = False
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) -> Tensor:
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if dtype is None:
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dtype = torch.get_default_dtype()
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_window_function_checks('cosine', M, dtype, layout)
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if M == 0:
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return torch.empty((0,), dtype=dtype, layout=layout, device=device, requires_grad=requires_grad)
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start = 0.5
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constant = torch.pi / (M + 1 if not sym and M > 1 else M)
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k = torch.linspace(start=start * constant,
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end=(start + (M - 1)) * constant,
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steps=M,
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dtype=dtype,
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layout=layout,
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device=device,
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requires_grad=requires_grad)
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return torch.sin(k)
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@_add_docstr(
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r"""
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Computes a window with a gaussian waveform.
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The gaussian window is defined as follows:
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.. math::
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w_n = \exp{\left(-\left(\frac{n}{2\sigma}\right)^2\right)}
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""",
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r"""
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{normalization}
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Args:
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{M}
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Keyword args:
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std (float, optional): the standard deviation of the gaussian. It controls how narrow or wide the window is.
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Default: 1.0.
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{sym}
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{dtype}
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{layout}
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{device}
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{requires_grad}
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Examples::
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>>> # Generates a symmetric gaussian window with a standard deviation of 1.0.
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>>> torch.signal.windows.gaussian(10)
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tensor([4.0065e-05, 2.1875e-03, 4.3937e-02, 3.2465e-01, 8.8250e-01, 8.8250e-01, 3.2465e-01, 4.3937e-02, 2.1875e-03, 4.0065e-05])
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>>> # Generates a periodic gaussian window and standard deviation equal to 0.9.
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>>> torch.signal.windows.gaussian(10, sym=False,std=0.9)
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tensor([1.9858e-07, 5.1365e-05, 3.8659e-03, 8.4658e-02, 5.3941e-01, 1.0000e+00, 5.3941e-01, 8.4658e-02, 3.8659e-03, 5.1365e-05])
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""".format(
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**window_common_args,
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),
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)
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def gaussian(
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M: int,
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*,
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std: float = 1.0,
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sym: bool = True,
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dtype: Optional[torch.dtype] = None,
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layout: torch.layout = torch.strided,
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device: Optional[torch.device] = None,
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requires_grad: bool = False
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) -> Tensor:
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if dtype is None:
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dtype = torch.get_default_dtype()
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_window_function_checks('gaussian', M, dtype, layout)
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if std <= 0:
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raise ValueError(f'Standard deviation must be positive, got: {std} instead.')
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if M == 0:
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return torch.empty((0,), dtype=dtype, layout=layout, device=device, requires_grad=requires_grad)
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start = -(M if not sym and M > 1 else M - 1) / 2.0
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constant = 1 / (std * sqrt(2))
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k = torch.linspace(start=start * constant,
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end=(start + (M - 1)) * constant,
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steps=M,
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dtype=dtype,
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layout=layout,
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device=device,
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requires_grad=requires_grad)
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return torch.exp(-k ** 2)
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@_add_docstr(
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r"""
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Computes the Kaiser window.
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The Kaiser window is defined as follows:
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.. math::
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w_n = I_0 \left( \beta \sqrt{1 - \left( {\frac{n - N/2}{N/2}} \right) ^2 } \right) / I_0( \beta )
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where ``I_0`` is the zeroth order modified Bessel function of the first kind (see :func:`torch.special.i0`), and
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``N = M - 1 if sym else M``.
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""",
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r"""
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{normalization}
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Args:
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{M}
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Keyword args:
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beta (float, optional): shape parameter for the window. Must be non-negative. Default: 12.0
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{sym}
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{dtype}
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{layout}
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{device}
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{requires_grad}
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Examples::
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>>> # Generates a symmetric gaussian window with a standard deviation of 1.0.
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>>> torch.signal.windows.kaiser(5)
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tensor([4.0065e-05, 2.1875e-03, 4.3937e-02, 3.2465e-01, 8.8250e-01, 8.8250e-01, 3.2465e-01, 4.3937e-02, 2.1875e-03, 4.0065e-05])
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>>> # Generates a periodic gaussian window and standard deviation equal to 0.9.
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>>> torch.signal.windows.kaiser(5, sym=False,std=0.9)
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tensor([1.9858e-07, 5.1365e-05, 3.8659e-03, 8.4658e-02, 5.3941e-01, 1.0000e+00, 5.3941e-01, 8.4658e-02, 3.8659e-03, 5.1365e-05])
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""".format(
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**window_common_args,
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),
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)
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def kaiser(
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M: int,
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*,
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beta: float = 12.0,
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sym: bool = True,
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dtype: Optional[torch.dtype] = None,
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layout: torch.layout = torch.strided,
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device: Optional[torch.device] = None,
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requires_grad: bool = False
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) -> Tensor:
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if dtype is None:
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dtype = torch.get_default_dtype()
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_window_function_checks('kaiser', M, dtype, layout)
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if beta < 0:
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raise ValueError(f'beta must be non-negative, got: {beta} instead.')
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if M == 0:
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return torch.empty((0,), dtype=dtype, layout=layout, device=device, requires_grad=requires_grad)
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if M == 1:
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return torch.ones((1,), dtype=dtype, layout=layout, device=device, requires_grad=requires_grad)
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# Avoid NaNs by casting `beta` to the appropriate dtype.
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beta = torch.tensor(beta, dtype=dtype, device=device)
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start = -beta
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constant = 2.0 * beta / (M if not sym else M - 1)
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end = torch.minimum(beta, start + (M - 1) * constant)
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k = torch.linspace(start=start,
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end=end,
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steps=M,
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dtype=dtype,
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layout=layout,
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device=device,
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requires_grad=requires_grad)
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return torch.i0(torch.sqrt(beta * beta - torch.pow(k, 2))) / torch.i0(beta)
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@_add_docstr(
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r"""
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Computes the Hamming window.
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The Hamming window is defined as follows:
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.. math::
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w_n = \alpha - \beta\ \cos \left( \frac{2 \pi n}{M - 1} \right)
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""",
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r"""
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{normalization}
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Arguments:
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{M}
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Keyword args:
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{sym}
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alpha (float, optional): The coefficient :math:`\alpha` in the equation above.
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beta (float, optional): The coefficient :math:`\beta` in the equation above.
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{dtype}
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{layout}
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{device}
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{requires_grad}
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Examples::
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>>> # Generates a symmetric Hamming window.
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>>> torch.signal.windows.hamming(10)
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tensor([0.0800, 0.1876, 0.4601, 0.7700, 0.9723, 0.9723, 0.7700, 0.4601, 0.1876, 0.0800])
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>>> # Generates a periodic Hamming window.
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>>> torch.signal.windows.hamming(10, sym=False)
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tensor([0.0800, 0.1679, 0.3979, 0.6821, 0.9121, 1.0000, 0.9121, 0.6821, 0.3979, 0.1679])
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""".format(
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**window_common_args
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),
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)
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def hamming(M: int,
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*,
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sym: bool = True,
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dtype: Optional[torch.dtype] = None,
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layout: torch.layout = torch.strided,
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device: Optional[torch.device] = None,
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requires_grad: bool = False) -> Tensor:
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return general_hamming(M, sym=sym, dtype=dtype, layout=layout, device=device, requires_grad=requires_grad)
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@_add_docstr(
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r"""
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Computes the Hann window.
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The Hann window is defined as follows:
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.. math::
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w_n = \frac{1}{2}\ \left[1 - \cos \left( \frac{2 \pi n}{M - 1} \right)\right] =
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\sin^2 \left( \frac{\pi n}{M - 1} \right)
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""",
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r"""
|
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|
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{normalization}
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Arguments:
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{M}
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Keyword args:
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{sym}
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{dtype}
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{layout}
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{device}
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{requires_grad}
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Examples::
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>>> # Generates a symmetric Hann window.
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>>> torch.signal.windows.hann(10)
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tensor([0.0000, 0.1170, 0.4132, 0.7500, 0.9698, 0.9698, 0.7500, 0.4132, 0.1170, 0.0000])
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>>> # Generates a periodic Hann window.
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>>> torch.signal.windows.hann(10, sym=False)
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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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""".format(
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**window_common_args
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),
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)
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def hann(M: int,
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*,
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sym: bool = True,
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dtype: Optional[torch.dtype] = None,
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layout: torch.layout = torch.strided,
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device: Optional[torch.device] = None,
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requires_grad: bool = False) -> Tensor:
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return general_hamming(M,
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alpha=0.5,
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sym=sym,
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dtype=dtype,
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layout=layout,
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device=device,
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requires_grad=requires_grad)
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@_add_docstr(
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r"""
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Computes the Blackman window.
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The Blackman window is defined as follows:
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.. math::
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w_n = 0.42 - 0.5 \cos \left( \frac{2 \pi n}{M - 1} \right) + 0.08 \cos \left( \frac{4 \pi n}{M - 1} \right)
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""",
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r"""
|
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{normalization}
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Arguments:
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{M}
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Keyword args:
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{sym}
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{dtype}
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{layout}
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{device}
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{requires_grad}
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Examples::
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>>> # Generates a symmetric Blackman window.
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>>> torch.signal.windows.blackman(5)
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tensor([-1.4901e-08, 3.4000e-01, 1.0000e+00, 3.4000e-01, -1.4901e-08])
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>>> # Generates a periodic Blackman window.
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>>> torch.signal.windows.blackman(5, sym=False)
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tensor([-1.4901e-08, 2.0077e-01, 8.4923e-01, 8.4923e-01, 2.0077e-01])
|
|
""".format(
|
|
**window_common_args
|
|
),
|
|
)
|
|
def blackman(M: int,
|
|
*,
|
|
sym: bool = True,
|
|
dtype: Optional[torch.dtype] = None,
|
|
layout: torch.layout = torch.strided,
|
|
device: Optional[torch.device] = None,
|
|
requires_grad: bool = False) -> Tensor:
|
|
if dtype is None:
|
|
dtype = torch.get_default_dtype()
|
|
|
|
_window_function_checks('blackman', M, dtype, layout)
|
|
|
|
return general_cosine(M, a=[0.42, 0.5, 0.08], sym=sym, dtype=dtype, layout=layout, device=device,
|
|
requires_grad=requires_grad)
|
|
|
|
|
|
@_add_docstr(
|
|
r"""
|
|
Computes the Bartlett window.
|
|
|
|
The Bartlett window is defined as follows:
|
|
|
|
.. math::
|
|
w_n = 1 - \left| \frac{2n}{M - 1} - 1 \right| = \begin{cases}
|
|
\frac{2n}{M - 1} & \text{if } 0 \leq n \leq \frac{M - 1}{2} \\
|
|
2 - \frac{2n}{M - 1} & \text{if } \frac{M - 1}{2} < n < M \\ \end{cases}
|
|
""",
|
|
r"""
|
|
|
|
{normalization}
|
|
|
|
Arguments:
|
|
{M}
|
|
|
|
Keyword args:
|
|
{sym}
|
|
{dtype}
|
|
{layout}
|
|
{device}
|
|
{requires_grad}
|
|
|
|
Examples::
|
|
|
|
>>> # Generates a symmetric Bartlett window.
|
|
>>> torch.signal.windows.bartlett(10)
|
|
tensor([0.0000, 0.2222, 0.4444, 0.6667, 0.8889, 0.8889, 0.6667, 0.4444, 0.2222, 0.0000])
|
|
|
|
>>> # Generates a periodic Bartlett window.
|
|
>>> torch.signal.windows.bartlett(10, sym=False)
|
|
tensor([0.0000, 0.2000, 0.4000, 0.6000, 0.8000, 1.0000, 0.8000, 0.6000, 0.4000, 0.2000])
|
|
""".format(
|
|
**window_common_args
|
|
),
|
|
)
|
|
def bartlett(M: int,
|
|
*,
|
|
sym: bool = True,
|
|
dtype: Optional[torch.dtype] = None,
|
|
layout: torch.layout = torch.strided,
|
|
device: Optional[torch.device] = None,
|
|
requires_grad: bool = False) -> Tensor:
|
|
if dtype is None:
|
|
dtype = torch.get_default_dtype()
|
|
|
|
_window_function_checks('bartlett', M, dtype, layout)
|
|
|
|
if M == 0:
|
|
return torch.empty((0,), dtype=dtype, layout=layout, device=device, requires_grad=requires_grad)
|
|
|
|
if M == 1:
|
|
return torch.ones((1,), dtype=dtype, layout=layout, device=device, requires_grad=requires_grad)
|
|
|
|
start = -1
|
|
constant = 2 / (M if not sym else M - 1)
|
|
|
|
k = torch.linspace(start=start,
|
|
end=start + (M - 1) * constant,
|
|
steps=M,
|
|
dtype=dtype,
|
|
layout=layout,
|
|
device=device,
|
|
requires_grad=requires_grad)
|
|
|
|
return 1 - torch.abs(k)
|
|
|
|
|
|
@_add_docstr(
|
|
r"""
|
|
Computes the general cosine window.
|
|
|
|
The general cosine window is defined as follows:
|
|
|
|
.. math::
|
|
w_n = \sum^{M-1}_{i=0} (-1)^i a_i \cos{ \left( \frac{2 \pi i n}{M - 1}\right)}
|
|
""",
|
|
r"""
|
|
|
|
{normalization}
|
|
|
|
Arguments:
|
|
{M}
|
|
|
|
Keyword args:
|
|
a (Iterable): the coefficients associated to each of the cosine functions.
|
|
{sym}
|
|
{dtype}
|
|
{layout}
|
|
{device}
|
|
{requires_grad}
|
|
|
|
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])
|
|
""".format(
|
|
**window_common_args
|
|
),
|
|
)
|
|
def general_cosine(M, *,
|
|
a: Iterable,
|
|
sym: bool = True,
|
|
dtype: Optional[torch.dtype] = None,
|
|
layout: torch.layout = torch.strided,
|
|
device: Optional[torch.device] = None,
|
|
requires_grad: bool = False) -> Tensor:
|
|
if dtype is None:
|
|
dtype = torch.get_default_dtype()
|
|
|
|
_window_function_checks('general_cosine', M, dtype, layout)
|
|
|
|
if M == 0:
|
|
return torch.empty((0,), dtype=dtype, layout=layout, device=device, requires_grad=requires_grad)
|
|
|
|
if M == 1:
|
|
return torch.ones((1,), dtype=dtype, layout=layout, device=device, requires_grad=requires_grad)
|
|
|
|
if not isinstance(a, Iterable):
|
|
raise TypeError("Coefficients must be a list/tuple")
|
|
|
|
if not a:
|
|
raise ValueError("Coefficients cannot be empty")
|
|
|
|
constant = 2 * torch.pi / (M if not sym else M - 1)
|
|
|
|
k = torch.linspace(start=0,
|
|
end=(M - 1) * constant,
|
|
steps=M,
|
|
dtype=dtype,
|
|
layout=layout,
|
|
device=device,
|
|
requires_grad=requires_grad)
|
|
|
|
a_i = torch.tensor([(-1) ** i * w for i, w in enumerate(a)], device=device, dtype=dtype, requires_grad=requires_grad)
|
|
i = torch.arange(a_i.shape[0], dtype=a_i.dtype, device=a_i.device, requires_grad=a_i.requires_grad)
|
|
return (a_i.unsqueeze(-1) * torch.cos(i.unsqueeze(-1) * k)).sum(0)
|
|
|
|
|
|
@_add_docstr(
|
|
r"""
|
|
Computes the general Hamming window.
|
|
|
|
The general Hamming window is defined as follows:
|
|
|
|
.. math::
|
|
w_n = \alpha - (1 - \alpha) \cos{ \left( \frac{2 \pi n}{M-1} \right)}
|
|
""",
|
|
r"""
|
|
|
|
{normalization}
|
|
|
|
Arguments:
|
|
{M}
|
|
|
|
Keyword args:
|
|
alpha (float, optional): the window coefficient. Default: 0.54.
|
|
{sym}
|
|
{dtype}
|
|
{layout}
|
|
{device}
|
|
{requires_grad}
|
|
|
|
Examples::
|
|
|
|
>>> # Generates a symmetric Hamming window with the general Hamming window.
|
|
>>> torch.signal.windows.general_hamming(10, sym=True)
|
|
tensor([0.0800, 0.1876, 0.4601, 0.7700, 0.9723, 0.9723, 0.7700, 0.4601, 0.1876, 0.0800])
|
|
|
|
>>> # Generates a periodic Hann window with the general Hamming window.
|
|
>>> torch.signal.windows.general_hamming(10, alpha=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])
|
|
""".format(
|
|
**window_common_args
|
|
),
|
|
)
|
|
def general_hamming(M,
|
|
*,
|
|
alpha: float = 0.54,
|
|
sym: bool = True,
|
|
dtype: Optional[torch.dtype] = None,
|
|
layout: torch.layout = torch.strided,
|
|
device: Optional[torch.device] = None,
|
|
requires_grad: bool = False) -> Tensor:
|
|
return general_cosine(M,
|
|
a=[alpha, 1. - alpha],
|
|
sym=sym,
|
|
dtype=dtype,
|
|
layout=layout,
|
|
device=device,
|
|
requires_grad=requires_grad)
|
|
|
|
|
|
@_add_docstr(
|
|
r"""
|
|
Computes the minimum 4-term Blackman-Harris window according to Nuttall.
|
|
|
|
.. math::
|
|
w_n = 1 - 0.36358 \cos{(z_n)} + 0.48917 \cos{(2z_n)} - 0.13659 \cos{(3z_n)} + 0.01064 \cos{(4z_n)}
|
|
|
|
where ``z_n = 2 π n/ M``.
|
|
""",
|
|
"""
|
|
|
|
{normalization}
|
|
|
|
Arguments:
|
|
{M}
|
|
|
|
Keyword args:
|
|
{sym}
|
|
{dtype}
|
|
{layout}
|
|
{device}
|
|
{requires_grad}
|
|
|
|
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])
|
|
""".format(
|
|
**window_common_args
|
|
),
|
|
)
|
|
def nuttall(
|
|
M: int,
|
|
*,
|
|
sym: bool = True,
|
|
dtype: Optional[torch.dtype] = None,
|
|
layout: torch.layout = torch.strided,
|
|
device: Optional[torch.device] = None,
|
|
requires_grad: bool = False
|
|
) -> Tensor:
|
|
return general_cosine(M,
|
|
a=[0.3635819, 0.4891775, 0.1365995, 0.0106411],
|
|
sym=sym,
|
|
dtype=dtype,
|
|
layout=layout,
|
|
device=device,
|
|
requires_grad=requires_grad)
|