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Hamming in Numpy
• Last Updated : 26 Jun, 2019

The Hamming window is a taper formed by 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```

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.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 (requires SciPy and matplotlib):
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() `

Output: 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()`

Output: hamming_frequency

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