code: def blackman(M, sym=True):
      r"""
      Return a Blackman window.
  
      The Blackman window is a taper formed by using the first three terms of
      a summation of cosines. It was designed to have close to the minimal
      leakage possible.  It is close to optimal, only slightly worse than a
      Kaiser 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).
  
      Notes
      -----
      The Blackman window is defined as
  
      .. math::  w(n) = 0.42 - 0.5 \cos(2\pi n/M) + 0.08 \cos(4\pi n/M)
  
      The "exact Blackman" window was designed to null out the third and fourth
      sidelobes, but has discontinuities at the boundaries, resulting in a
      6 dB/oct fall-off.  This window is an approximation of the "exact" window,
      which does not null the sidelobes as well, but is smooth at the edges,
      improving the fall-off rate to 18 dB/oct. [3]_
  
      Most references to the Blackman 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. It is known as a
      "near optimal" tapering function, almost as good (by some measures)
      as the Kaiser window.
  
      References
      ----------
      .. [1] Blackman, R.B. and Tukey, J.W., (1958) The measurement of power
             spectra, Dover Publications, New York.
      .. [2] Oppenheim, A.V., and R.W. Schafer. Discrete-Time Signal Processing.
             Upper Saddle River, NJ: Prentice-Hall, 1999, pp. 468-471.
      .. [3] Harris, Fredric J. (Jan 1978). "On the use of Windows for Harmonic
             Analysis with the Discrete Fourier Transform". Proceedings of the
             IEEE 66 (1): 51-83. :doi:`10.1109/PROC.1978.10837`.
  
      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.blackman(51)
      >>> plt.plot(window)
      >>> plt.title("Blackman 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 Blackman window")
      >>> plt.ylabel("Normalized magnitude [dB]")
      >>> plt.xlabel("Normalized frequency [cycles per sample]")
  
      """
      # Docstring adapted from NumPy's blackman function
      return general_cosine(M, [0.42, 0.50, 0.08], sym)
  
imports: from __future__ import division, print_function, absolute_import
  import operator
  import warnings
  import numpy
  from scipy import fftpack, linalg, special
  from scipy._lib.six import string_types
label_desc: Use image node_id 9 for calling a global function with example usage: blackman(M, sym) and returns: general_cosine