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# Hamming in Numpy

Hamming — this is a cone formed using a weighted cosine

`  Parameters (numpy.hamming (M)):   M:  int Number of points in the output window. If zero or less, an empty array is returned.  Returns:   out: array`

Window with maximum value normalized to one (value one appears only if the number of samples is odd).
Example :

` `

` import numpy as np  print (np.hamming ( 12 )) `

Output:

` [0.08 0.15302337 0.34890909 0.60546483 0.84123594 0.98136677 0.98136677 0.84123594 0.60546483 0.34890909 0.15302337 0.08] `

Plotting the window and its frequency response (SciPy required ):
For window:

 ` import ` ` numpy as np ` ` import ` ` matplotlib.pyplot as plt ` ` from ` ` numpy.fft ` ` import ` ` fft, fftshift `   ` window ` ` = ` ` np.hamming (` ` 51 ` `) `   ` plt.plot (window) ` ` plt.title (` `" Hamming window "` `) ` ` plt.ylabel (` ` "Amplitude" ` `) ` ` plt.xlabel (` ` "Sample" ` `) ` ` plt.show () `

Exit:

hamming_window

For frequency:

 ` import ` ` numpy as np ` ` import ` ` matplotlib.pyplot as plt ` ` from ` ` numpy.fft ` ` import ` ` fft, fftshift ` ` `  ` window ` ` = ` ` np.hamming (` ` 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 Hamming window" ` `) ` ` plt.ylabel (` ` "Magnitude [dB]" ` `) ` ` plt.xlabel (` ` "Normalized frequency [cycles per sample] "` `) ` ` plt.axis (` `’ tight’ ` `) ` ` plt.show () `

Exit:

hamming_frequency

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