parameters: (M, sym=True)
return_statement: return _truncate(w, needs_trunc)
docstring: Return a Parzen window.
  
      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).
  
      References
      ----------
      .. [1] E. Parzen, "Mathematical Considerations in the Estimation of
             Spectra", Technometrics,  Vol. 3, No. 2 (May, 1961), pp. 167-190
  
      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.parzen(51)
      >>> plt.plot(window)
      >>> plt.title("Parzen 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 Parzen window")
      >>> plt.ylabel("Normalized magnitude [dB]")
      >>> plt.xlabel("Normalized frequency [cycles per sample]")
docstring_summary: Return a Parzen window.
docstring_tokens: Return a Parzen window .
function: def parzen(M, sym=True):
      """Return a Parzen window.
  
      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).
  
      References
      ----------
      .. [1] E. Parzen, "Mathematical Considerations in the Estimation of
             Spectra", Technometrics,  Vol. 3, No. 2 (May, 1961), pp. 167-190
  
      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.parzen(51)
      >>> plt.plot(window)
      >>> plt.title("Parzen 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 Parzen window")
      >>> plt.ylabel("Normalized magnitude [dB]")
      >>> plt.xlabel("Normalized frequency [cycles per sample]")
  
      """
      if _len_guards(M):
          return np.ones(M)
      M, needs_trunc = _extend(M, sym)
  
      n = np.arange(-(M - 1) / 2.0, (M - 1) / 2.0 + 0.5, 1.0)
      na = np.extract(n < -(M - 1) / 4.0, n)
      nb = np.extract(abs(n) <= (M - 1) / 4.0, n)
      wa = 2 * (1 - np.abs(na) / (M / 2.0)) ** 3.0
      wb = (1 - 6 * (np.abs(nb) / (M / 2.0)) ** 2.0 +
            6 * (np.abs(nb) / (M / 2.0)) ** 3.0)
      w = np.r_[wa, wb, wa[::-1]]
  
      return _truncate(w, needs_trunc)
function_tokens: def parzen ( M , sym = True ) : if _len_guards ( M ) : return np . ones ( M ) M , needs_trunc = _extend ( M , sym ) n = np . arange ( - ( M - 1 ) / 2.0 , ( M - 1 ) / 2.0 + 0.5 , 1.0 ) na = np . extract ( n < - ( M - 1 ) / 4.0 , n ) nb = np . extract ( abs ( n ) <= ( M - 1 ) / 4.0 , n ) wa = 2 * ( 1 - np . abs ( na ) / ( M / 2.0 ) ) ** 3.0 wb = ( 1 - 6 * ( np . abs ( nb ) / ( M / 2.0 ) ) ** 2.0 + 6 * ( np . abs ( nb ) / ( M / 2.0 ) ) ** 3.0 ) w = np . r_ [ wa , wb , wa [ : : - 1 ] ] return _truncate ( w , needs_trunc )