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Update docs
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‎torch/signal/windows/__init__.py‎

Lines changed: 117 additions & 108 deletions
Original file line numberDiff line numberDiff line change
@@ -15,7 +15,9 @@ def _add_docstr(function, docstr):
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function.__doc__ = docstr
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_add_docstr(cosine, r"""
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_add_docstr(
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cosine,
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r"""
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Computes a window with a simple cosine waveform.
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Also known as the sine window.
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@@ -26,39 +28,42 @@ def _add_docstr(function, docstr):
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Where `M` is the length of the window.
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""" +
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r"""
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Args:
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window_length (int): the length of the output window.
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In other words, the number of points of the cosine window.
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periodic (bool, optional): If `True`, returns a periodic window suitable for use in spectral analysis.
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If `False`, returns a symmetric window suitable for use in filter design. Default: `True`.
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Keyword args:
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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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>>> # Generate a cosine window without keyword args.
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>>> torch.signal.windows.cosine(10)
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tensor([0.1423, 0.4154, 0.6549, 0.8413, 0.9595, 1.0000, 0.9595, 0.8413, 0.6549,
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0.4154])
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>>> # Generate a symmetric cosine window.
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>>> torch.signal.windows.cosine(10,periodic=False)
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tensor([0.1564, 0.4540, 0.7071, 0.8910, 0.9877, 0.9877, 0.8910, 0.7071, 0.4540,
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0.1564])
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.. note::
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The window is normalized to 1 (maximum value is 1), however, the 1 doesn't appear if `M` is even
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and `periodic` is `False`.
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""".format(
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**factory_common_args
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))
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_add_docstr(exponential, r"""
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r"""
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Args:
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window_length (int): the length of the output window.
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In other words, the number of points of the cosine window.
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periodic (bool, optional): If `True`, returns a periodic window suitable for use in spectral analysis.
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If `False`, returns a symmetric window suitable for use in filter design. Default: `True`.
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Keyword args:
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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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>>> # Generate a cosine window without keyword args.
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>>> torch.signal.windows.cosine(10)
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tensor([0.1423, 0.4154, 0.6549, 0.8413, 0.9595, 1.0000, 0.9595, 0.8413, 0.6549,
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0.4154])
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>>> # Generate a symmetric cosine window.
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>>> torch.signal.windows.cosine(10,periodic=False)
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tensor([0.1564, 0.4540, 0.7071, 0.8910, 0.9877, 0.9877, 0.8910, 0.7071, 0.4540,
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0.1564])
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.. note::
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The window is normalized to 1 (maximum value is 1), however, the 1 doesn't appear if `M` is even
58+
and `periodic` is `False`.
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""".format(
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**factory_common_args
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)
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)
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_add_docstr(
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exponential,
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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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@@ -67,84 +72,88 @@ def _add_docstr(function, docstr):
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.. math::
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w(n) = \exp{\left(-\frac{|n - center|}{\tau}\right)}
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""" +
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r"""
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Args:
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window_length (int): the length of the output window.
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In other words, the number of points of the ee window.
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periodic (bool, optional): If `True`, returns a periodic window suitable for use in spectral analysis.
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If `False`, returns a symmetric window suitable for use in filter design. Default: `True`.
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center (float, optional): his value defines where the center of the window will be located.
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In other words, at which sample the peak of the window can be found.
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Default: `window_length / 2` if `periodic` is `True` (default), else `(window_length - 1) / 2`.
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tau (float, optional): the decay value.
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For `center = 0`, it's suggested to use :math:`\tau = -\frac{(M - 1)}{\ln(x)}`,
82-
if `x` is the fraction of the window remaining at the end. Default: 1.0.
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""" +
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r"""
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Keyword args:
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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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>>> # Generate an exponential window without keyword args.
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>>> torch.signal.windows.exponential(10)
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tensor([0.0067, 0.0183, 0.0498, 0.1353, 0.3679, 1.0000, 0.3679, 0.1353, 0.0498,
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0.0183])
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>>> # Generate a symmetric exponential window and decay factor equal to .5
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>>> torch.signal.windows.exponential(10,periodic=False,tau=.5)
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tensor([1.2341e-04, 9.1188e-04, 6.7379e-03, 4.9787e-02, 3.6788e-01, 3.6788e-01,
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4.9787e-02, 6.7379e-03, 9.1188e-04, 1.2341e-04])
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.. note::
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The window is normalized to 1 (maximum value is 1), however, the 1 doesn't appear if `M` is even
105-
and `periodic` is `False`.
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""".format(
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**factory_common_args
108-
))
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_add_docstr(gaussian, r"""
75+
r"""
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Args:
78+
window_length (int): the length of the output window.
79+
In other words, the number of points of the ee window.
80+
periodic (bool, optional): If `True`, returns a periodic window suitable for use in spectral analysis.
81+
If `False`, returns a symmetric window suitable for use in filter design. Default: `True`.
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center (float, optional): his value defines where the center of the window will be located.
83+
In other words, at which sample the peak of the window can be found.
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Default: `window_length / 2` if `periodic` is `True` (default), else `(window_length - 1) / 2`.
85+
tau (float, optional): the decay value.
86+
For `center = 0`, it's suggested to use :math:`\tau = -\frac{(M - 1)}{\ln(x)}`,
87+
if `x` is the fraction of the window remaining at the end. Default: 1.0.
88+
""" +
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r"""
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Keyword args:
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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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>>> # Generate an exponential window without keyword args.
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>>> torch.signal.windows.exponential(10)
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tensor([0.0067, 0.0183, 0.0498, 0.1353, 0.3679, 1.0000, 0.3679, 0.1353, 0.0498,
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0.0183])
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>>> # Generate a symmetric exponential window and decay factor equal to .5
104+
>>> torch.signal.windows.exponential(10,periodic=False,tau=.5)
105+
tensor([1.2341e-04, 9.1188e-04, 6.7379e-03, 4.9787e-02, 3.6788e-01, 3.6788e-01,
106+
4.9787e-02, 6.7379e-03, 9.1188e-04, 1.2341e-04])
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.. note::
109+
The window is normalized to 1 (maximum value is 1), however, the 1 doesn't appear if `M` is even
110+
and `periodic` is `False`.
111+
""".format(
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**factory_common_args
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)
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)
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_add_docstr(
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gaussian,
118+
r"""
111119
Computes a window with a gaussian waveform.
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The gaussian window is defined as follows:
114122
115123
.. math::
116124
w(n) = \exp{\left(-\left(\frac{n}{2\sigma}\right)^2\right)}
117125
""" +
118-
r"""
119-
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Args:
121-
window_length (int): the length of the output window.
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In other words, the number of points of the cosine window.
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periodic (bool, optional): If `True`, returns a periodic window suitable for use in spectral analysis.
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If `False`, returns a symmetric window suitable for use in filter design. Default: `True`
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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: 0.5.
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Keyword args:
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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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>>> # Generate a gaussian window without keyword args.
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>>> torch.signal.windows.gaussian(10)
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tensor([1.9287e-22, 1.2664e-14, 1.5230e-08, 3.3546e-04, 1.3534e-01, 1.0000e+00,
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1.3534e-01, 3.3546e-04, 1.5230e-08, 1.2664e-14])
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>>> # Generate a symmetric gaussian window and standard deviation equal to 0.9.
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>>> torch.signal.windows.gaussian(10,periodic=False,std=0.9)
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tensor([3.7267e-06, 5.1998e-04, 2.1110e-02, 2.4935e-01, 8.5700e-01, 8.5700e-01,
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2.4935e-01, 2.1110e-02, 5.1998e-04, 3.7267e-06])
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.. note::
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The window is normalized to 1 (maximum value is 1), however, the 1 doesn't appear if `M` is even
147-
and `periodic` is `False`.
148-
""".format(
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**factory_common_args
150-
))
126+
r"""
127+
128+
Args:
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window_length (int): the length of the output window.
130+
In other words, the number of points of the cosine window.
131+
periodic (bool, optional): If `True`, returns a periodic window suitable for use in spectral analysis.
132+
If `False`, returns a symmetric window suitable for use in filter design. Default: `True`
133+
std (float, optional): the standard deviation of the gaussian. It controls how narrow or wide the window is.
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Default: 0.5.
135+
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Keyword args:
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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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>>> # Generate a gaussian window without keyword args.
144+
>>> torch.signal.windows.gaussian(10)
145+
tensor([1.9287e-22, 1.2664e-14, 1.5230e-08, 3.3546e-04, 1.3534e-01, 1.0000e+00,
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1.3534e-01, 3.3546e-04, 1.5230e-08, 1.2664e-14])
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>>> # Generate a symmetric gaussian window and standard deviation equal to 0.9.
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>>> torch.signal.windows.gaussian(10,periodic=False,std=0.9)
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tensor([3.7267e-06, 5.1998e-04, 2.1110e-02, 2.4935e-01, 8.5700e-01, 8.5700e-01,
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2.4935e-01, 2.1110e-02, 5.1998e-04, 3.7267e-06])
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.. note::
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The window is normalized to 1 (maximum value is 1), however, the 1 doesn't appear if `M` is even
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and `periodic` is `False`.
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""".format(
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**factory_common_args
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)
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)

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