text: def selection_8():
  
      # Library import
      import numpy
      import matplotlib
      import matplotlib.pyplot   as plt
      import matplotlib.gridspec as gridspec
  
      # Library version
      matplotlib_version = matplotlib.__version__
      numpy_version      = numpy.__version__
  
      # Histo binning
      xBinning = numpy.linspace(-15.0,15.0,101,endpoint=True)
  
      # Creating data sequence: middle of each bin
      xData = numpy.array([-14.85,-14.55,-14.25,-13.95,-13.65,-13.35,-13.05,-12.75,-12.45,-12.15,-11.85,-11.55,-11.25,-10.95,-10.65,-10.35,-10.05,-9.75,-9.45,-9.15,-8.85,-8.55,-8.25,-7.95,-7.65,-7.35,-7.05,-6.75,-6.45,-6.15,-5.85,-5.55,-5.25,-4.95,-4.65,-4.35,-4.05,-3.75,-3.45,-3.15,-2.85,-2.55,-2.25,-1.95,-1.65,-1.35,-1.05,-0.75,-0.45,-0.15,0.15,0.45,0.75,1.05,1.35,1.65,1.95,2.25,2.55,2.85,3.15,3.45,3.75,4.05,4.35,4.65,4.95,5.25,5.55,5.85,6.15,6.45,6.75,7.05,7.35,7.65,7.95,8.25,8.55,8.85,9.15,9.45,9.75,10.05,10.35,10.65,10.95,11.25,11.55,11.85,12.15,12.45,12.75,13.05,13.35,13.65,13.95,14.25,14.55,14.85])
  
      # Creating weights for histo: y9_sdETA_0
      y9_sdETA_0_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.74328964206,0.37164486103,1.11493446309,2.60151414721,2.60151414721,8.17618846266,7.80454044163,12.264280694,15.2374408622,18.5822450515,18.5822450515,24.900205409,43.8540824816,43.8540824816,58.7198833228,70.9841640167,93.6545252996,121.527886877,156.462488854,216.29733224,280.591895878,353.805940021,307.350297392,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,301.403977055,370.901580988,266.469375079,213.695812092,152.002768601,126.35924715,90.6813651313,78.4170844374,57.6049632597,43.1108024395,36.421198061,30.8465257455,19.6971771146,15.9807289043,10.4060565888,10.4060565888,8.91947650472,8.17618846266,5.94631633648,3.34480378927,2.22986932618,1.48657968412,1.48657968412,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating a new Canvas
      fig   = plt.figure(figsize=(8,6),dpi=80)
      frame = gridspec.GridSpec(1,1)
      pad   = fig.add_subplot(frame[0])
  
      # Creating a new Stack
      pad.hist(x=xData, bins=xBinning, weights=y9_sdETA_0_weights,\
               label="$signal$", histtype="stepfilled", rwidth=1.0,\
               color="#5954d8", edgecolor="#5954d8", linewidth=1, linestyle="solid",\
               bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
  
  
      # Axis
      plt.rc('text',usetex=False)
      plt.xlabel(r"\Delta\eta ( j_{1} , j_{2} ) ",\
                 fontsize=16,color="black")
      plt.ylabel(r"$\mathrm{Events}$ $(\mathcal{L}_{\mathrm{int}} = 40.0\ \mathrm{fb}^{-1})$ ",\
                 fontsize=16,color="black")
  
      # Boundary of y-axis
      ymax=(y9_sdETA_0_weights).max()*1.1
      ymin=0 # linear scale
      #ymin=min([x for x in (y9_sdETA_0_weights) if x])/100. # log scale
      plt.gca().set_ylim(ymin,ymax)
  
      # Log/Linear scale for X-axis
      plt.gca().set_xscale("linear")
      #plt.gca().set_xscale("log",nonposx="clip")
  
      # Log/Linear scale for Y-axis
      plt.gca().set_yscale("linear")
      #plt.gca().set_yscale("log",nonposy="clip")
  
      # Saving the image
      plt.savefig('../../HTML/MadAnalysis5job_0/selection_8.png')
      plt.savefig('../../PDF/MadAnalysis5job_0/selection_8.png')
      plt.savefig('../../DVI/MadAnalysis5job_0/selection_8.eps')
  
  # Running!
  if __name__ == '__main__':
      selection_8()
  