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