scipy.signal.get_window#

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 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