get_window(window, Nx, fftbins=True)
Window types:
kaiser
(needs beta)
gaussian
(needs standard deviation)
general_cosine
(needs weighting coefficients)
general_gaussian
(needs power, width)
general_hamming
(needs window coefficient)
dpss
(needs normalized half-bandwidth)
chebwin
(needs attenuation)
If the window requires no parameters, then :None:None:`window` can be a string.
If the window requires parameters, then :None:None:`window` must be a tuple with the first argument the string name of the window, and the next arguments the needed parameters.
If :None:None:`window` is a floating point number, it is interpreted as the beta parameter of the kaiser
window.
Each of the window types listed above is also the name of a function that can be called directly to create a window of that type.
The type of window to create. See below for more details.
The number of samples in the window.
If True (default), create a "periodic" window, ready to use with :None:None:`ifftshift` and be multiplied by the result of an FFT (see also ~scipy.fft.fftfreq
). If False, create a "symmetric" window, for use in filter design.
Returns a window of length :None:None:`Nx` and type :None:None:`window`
Return a window of a given length and type.
>>> from scipy import signal
... signal.get_window('triang', 7) array([ 0.125, 0.375, 0.625, 0.875, 0.875, 0.625, 0.375])
>>> signal.get_window(('kaiser', 4.0), 9) array([ 0.08848053, 0.29425961, 0.56437221, 0.82160913, 0.97885093, 0.97885093, 0.82160913, 0.56437221, 0.29425961])
>>> signal.get_window(('exponential', None, 1.), 9) array([ 0.011109 , 0.03019738, 0.082085 , 0.22313016, 0.60653066, 0.60653066, 0.22313016, 0.082085 , 0.03019738])
>>> signal.get_window(4.0, 9) array([ 0.08848053, 0.29425961, 0.56437221, 0.82160913, 0.97885093, 0.97885093, 0.82160913, 0.56437221, 0.29425961])See :
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.signal._spectral_py._spectral_helperscipy.signal._spectral_py.periodogramscipy.signal.windows._windows.get_windowscipy.signal._fir_filter_design.firwinscipy.signal._spectral_py.check_NOLAscipy.signal._signaltools.resamplescipy.signal._spectral_py.check_COLAscipy.signal._spectral_py.coherencescipy.signal._spectral_py.stftscipy.signal._spectral_py.csdscipy.signal._spectral_py.welchscipy.signal._fir_filter_design.firwin2scipy.signal._signaltools.resample_polyscipy.signal._spectral_py.spectrogramscipy.signal._spectral_py.istftmatplotlib.axes._axes.Axes.coherematplotlib.pyplot.psdmatplotlib.pyplot.angle_spectrummatplotlib.axes._axes.Axes.magnitude_spectrummatplotlib.pyplot.coherematplotlib.mlab.coherematplotlib.mlab.specgrammatplotlib.pyplot.magnitude_spectrummatplotlib.axes._axes.Axes.psdmatplotlib.pyplot.phase_spectrummatplotlib.pyplot.csdmatplotlib.axes._axes.Axes.csdmatplotlib.mlab.csdmatplotlib.axes._axes.Axes.angle_spectrummatplotlib.axes._axes.Axes.phase_spectrummatplotlib.pyplot.specgrammatplotlib.axes._axes.Axes.specgrammatplotlib.mlab.psdHover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them