Python | Blackman in numpy
Blackman window : It is a taper formed by using the first three terms of a summation of cosines. It was designed to have close to the minimal leakage possible. It is close to optimal, only slightly worse than a Kaiser window.
Parameters(numpy.blackman):
M : int Number of points in the output window.
If zero or less, an empty array is returned.
Returns:
out : array
The window, with the maximum value normalized to one (the value one appears only if the number of samples is odd).
Example:
import numpy as np
print (np.blackman( 12 ))
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Output:
[ -1.38777878e-17 3.26064346e-02 1.59903635e-01 4.14397981e-01
7.36045180e-01 9.67046769e-01 9.67046769e-01 7.36045180e-01
4.14397981e-01 1.59903635e-01 3.26064346e-02 -1.38777878e-17]
Plotting the window and its frequency response (requires SciPy and matplotlib):
Code : For Window:
import numpy as np
import matplotlib.pyplot as plt
from numpy.fft import fft, fftshift
window = np.blackman( 51 )
plt.plot(window)
plt.title( "Blackman window" )
plt.ylabel( "Amplitude" )
plt.xlabel( "Sample" )
plt.show()
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Output:
Code : For frequency:
import numpy as np
import matplotlib.pyplot as plt
from numpy.fft import fft, fftshift
window = np.blackman( 51 )
plt.figure()
A = fft(window, 2048 ) / 25.5
mag = np. abs (fftshift(A))
freq = np.linspace( - 0.5 , 0.5 , len (A))
response = 20 * np.log10(mag)
response = np.clip(response, - 100 , 100 )
plt.plot(freq, response)
plt.title( "Frequency response of Blackman window" )
plt.ylabel( "Magnitude [dB]" )
plt.xlabel( "Normalized frequency [cycles per sample]" )
plt.axis( 'tight' )
plt.show()
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Output:
Last Updated :
22 Jul, 2021
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