parameters: (M, sym=True)
return_statement: return general_hamming(M, 0.54, sym)
docstring: r"""Return a Hamming window.
  
      The Hamming window is a taper formed by using a raised cosine with
      non-zero endpoints, optimized to minimize the nearest side lobe.
  
      Parameters
      ----------
      M : int
          Number of points in the output window. If zero or less, an empty
          array is returned.
      sym : bool, optional
          When True (default), generates a symmetric window, for use in filter
          design.
          When False, generates a periodic window, for use in spectral analysis.
  
      Returns
      -------
      w : ndarray
          The window, with the maximum value normalized to 1 (though the value 1
          does not appear if `M` is even and `sym` is True).
  
      Notes
      -----
      The Hamming window is defined as
  
      .. math::  w(n) = 0.54 - 0.46 \cos\left(\frac{2\pi{n}}{M-1}\right)
                 \qquad 0 \leq n \leq M-1
  
      The Hamming was named for R. W. Hamming, an associate of J. W. Tukey and
      is described in Blackman and Tukey. It was recommended for smoothing the
      truncated autocovariance function in the time domain.
      Most references to the Hamming window come from the signal processing
      literature, where it is used as one of many windowing functions for
      smoothing values.  It is also known as an apodization (which means
      "removing the foot", i.e. smoothing discontinuities at the beginning
      and end of the sampled signal) or tapering function.
  
      References
      ----------
      .. [1] Blackman, R.B. and Tukey, J.W., (1958) The measurement of power
             spectra, Dover Publications, New York.
      .. [2] E.R. Kanasewich, "Time Sequence Analysis in Geophysics", The
             University of Alberta Press, 1975, pp. 109-110.
      .. [3] Wikipedia, "Window function",
             https://en.wikipedia.org/wiki/Window_function
      .. [4] W.H. Press,  B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling,
             "Numerical Recipes", Cambridge University Press, 1986, page 425.
  
      Examples
      --------
      Plot the window and its frequency response:
  
      >>> from scipy import signal
      >>> from scipy.fftpack import fft, fftshift
      >>> import matplotlib.pyplot as plt
  
      >>> window = signal.hamming(51)
      >>> plt.plot(window)
      >>> plt.title("Hamming window")
      >>> plt.ylabel("Amplitude")
      >>> plt.xlabel("Sample")
  
      >>> plt.figure()
      >>> A = fft(window, 2048) / (len(window)/2.0)
      >>> freq = np.linspace(-0.5, 0.5, len(A))
      >>> response = 20 * np.log10(np.abs(fftshift(A / abs(A).max())))
      >>> plt.plot(freq, response)
      >>> plt.axis([-0.5, 0.5, -120, 0])
      >>> plt.title("Frequency response of the Hamming window")
      >>> plt.ylabel("Normalized magnitude [dB]")
      >>> plt.xlabel("Normalized frequency [cycles per sample]")
docstring_summary: r"""Return a Hamming window.
docstring_tokens: r Return a Hamming window .
function: def hamming(M, sym=True):
      r"""Return a Hamming window.
  
      The Hamming window is a taper formed by using a raised cosine with
      non-zero endpoints, optimized to minimize the nearest side lobe.
  
      Parameters
      ----------
      M : int
          Number of points in the output window. If zero or less, an empty
          array is returned.
      sym : bool, optional
          When True (default), generates a symmetric window, for use in filter
          design.
          When False, generates a periodic window, for use in spectral analysis.
  
      Returns
      -------
      w : ndarray
          The window, with the maximum value normalized to 1 (though the value 1
          does not appear if `M` is even and `sym` is True).
  
      Notes
      -----
      The Hamming window is defined as
  
      .. math::  w(n) = 0.54 - 0.46 \cos\left(\frac{2\pi{n}}{M-1}\right)
                 \qquad 0 \leq n \leq M-1
  
      The Hamming was named for R. W. Hamming, an associate of J. W. Tukey and
      is described in Blackman and Tukey. It was recommended for smoothing the
      truncated autocovariance function in the time domain.
      Most references to the Hamming window come from the signal processing
      literature, where it is used as one of many windowing functions for
      smoothing values.  It is also known as an apodization (which means
      "removing the foot", i.e. smoothing discontinuities at the beginning
      and end of the sampled signal) or tapering function.
  
      References
      ----------
      .. [1] Blackman, R.B. and Tukey, J.W., (1958) The measurement of power
             spectra, Dover Publications, New York.
      .. [2] E.R. Kanasewich, "Time Sequence Analysis in Geophysics", The
             University of Alberta Press, 1975, pp. 109-110.
      .. [3] Wikipedia, "Window function",
             https://en.wikipedia.org/wiki/Window_function
      .. [4] W.H. Press,  B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling,
             "Numerical Recipes", Cambridge University Press, 1986, page 425.
  
      Examples
      --------
      Plot the window and its frequency response:
  
      >>> from scipy import signal
      >>> from scipy.fftpack import fft, fftshift
      >>> import matplotlib.pyplot as plt
  
      >>> window = signal.hamming(51)
      >>> plt.plot(window)
      >>> plt.title("Hamming window")
      >>> plt.ylabel("Amplitude")
      >>> plt.xlabel("Sample")
  
      >>> plt.figure()
      >>> A = fft(window, 2048) / (len(window)/2.0)
      >>> freq = np.linspace(-0.5, 0.5, len(A))
      >>> response = 20 * np.log10(np.abs(fftshift(A / abs(A).max())))
      >>> plt.plot(freq, response)
      >>> plt.axis([-0.5, 0.5, -120, 0])
      >>> plt.title("Frequency response of the Hamming window")
      >>> plt.ylabel("Normalized magnitude [dB]")
      >>> plt.xlabel("Normalized frequency [cycles per sample]")
  
      """
      # Docstring adapted from NumPy's hamming function
      return general_hamming(M, 0.54, sym)
function_tokens: def hamming ( M , sym = True ) : # Docstring adapted from NumPy's hamming function return general_hamming ( M , 0.54 , sym )