text: def selection_0():
  
      # 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(0.0,400.0,41,endpoint=True)
  
      # Creating data sequence: middle of each bin
      xData = numpy.array([5.0,15.0,25.0,35.0,45.0,55.0,65.0,75.0,85.0,95.0,105.0,115.0,125.0,135.0,145.0,155.0,165.0,175.0,185.0,195.0,205.0,215.0,225.0,235.0,245.0,255.0,265.0,275.0,285.0,295.0,305.0,315.0,325.0,335.0,345.0,355.0,365.0,375.0,385.0,395.0])
  
      # Creating weights for histo: y1_M_0
      y1_M_0_weights = numpy.array([0.0,0.0,0.0,12.5760774307,28.2961792191,28.2961792191,22.0081385037,88.0325700149,144.624896453,88.0325700149,72.3124482265,135.19285538,119.472733592,116.328733234,138.336855738,128.904814665,163.489018599,163.489018599,150.912937168,166.633058957,169.777059314,176.06510003,169.777059314,257.809629329,182.353140745,188.641181461,235.801466826,163.489018599,216.93734468,220.081385037,229.51342611,267.241670402,185.497181103,191.785221818,242.089507541,166.633058957,235.801466826,251.521588614,201.217262891,194.929222176])
  
      # 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=y1_M_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"M [ j_{1} , j_{2} ]   ( GeV ) ",\
                 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=(y1_M_0_weights).max()*1.1
      ymin=0 # linear scale
      #ymin=min([x for x in (y1_M_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_0.png')
      plt.savefig('../../PDF/MadAnalysis5job_0/selection_0.png')
      plt.savefig('../../DVI/MadAnalysis5job_0/selection_0.eps')
  
  # Running!
  if __name__ == '__main__':
      selection_0()
  