get_window — SciPy v1.15.3 Manual (original) (raw)

scipy.signal.

scipy.signal.get_window(window, Nx, fftbins=True)[source]#

Return a window of a given length and type.

Parameters:

windowstring, float, or tuple

The type of window to create. See below for more details.

Nxint

The number of samples in the window.

fftbinsbool, optional

If True (default), create a “periodic” window, ready to use with_ifftshift_ and be multiplied by the result of an FFT (see alsofftfreq). If False, create a “symmetric” window, for use in filter design.

Returns:

get_windowndarray

Returns a window of length Nx and type window

Notes

Window types:

• boxcar
• triang
• blackman
• hamming
• hann
• bartlett
• flattop
• parzen
• bohman
• blackmanharris
• nuttall
• barthann
• cosine
• exponential
• tukey
• taylor
• lanczos
• kaiser (needs beta)
• kaiser_bessel_derived (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 window can be a string.

If the window requires parameters, then window must be a tuple with the first argument the string name of the window, and the next arguments the needed parameters.

If 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.

Examples

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])