scipy.signal.get_window

scipy.signal.get_window(window, Nx, fftbins=True)

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 also fftfreq). 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

• 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 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])

scipy.signal.unit_impulse scipy.signal.cascade