text: def selection_12():
  
      # 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(0.0,4000.0,81,endpoint=True)
  
      # Creating data sequence: middle of each bin
      xData = numpy.array([25.0,75.0,125.0,175.0,225.0,275.0,325.0,375.0,425.0,475.0,525.0,575.0,625.0,675.0,725.0,775.0,825.0,875.0,925.0,975.0,1025.0,1075.0,1125.0,1175.0,1225.0,1275.0,1325.0,1375.0,1425.0,1475.0,1525.0,1575.0,1625.0,1675.0,1725.0,1775.0,1825.0,1875.0,1925.0,1975.0,2025.0,2075.0,2125.0,2175.0,2225.0,2275.0,2325.0,2375.0,2425.0,2475.0,2525.0,2575.0,2625.0,2675.0,2725.0,2775.0,2825.0,2875.0,2925.0,2975.0,3025.0,3075.0,3125.0,3175.0,3225.0,3275.0,3325.0,3375.0,3425.0,3475.0,3525.0,3575.0,3625.0,3675.0,3725.0,3775.0,3825.0,3875.0,3925.0,3975.0])
  
      # Creating weights for histo: y13_THT_0
      y13_THT_0_weights = numpy.array([9.93661580303,114.726283364,170.418636082,193.438124289,227.596099025,232.422787177,210.517131638,240.219735738,205.690443486,195.294552031,199.749922666,184.527343059,187.868881025,158.537594442,132.547805863,135.889343829,115.840116032,105.444184616,96.16212583,80.1969911112,76.1128980327,64.2318563924,52.350814752,56.0636302744,35.6431129327,35.6431129327,31.5590038698,22.2769450834,24.5046383929,21.1630964306,14.480012506,14.8512980543,11.509756092,10.0246258869,6.68308392463,4.45539061512,4.82667216735,6.31180237239,2.97025921125,3.34154156271,3.34154156271,4.08410506679,1.48512980543,0.74256470291,2.22769450834,0.74256470291,1.48512980543,1.48512980543,1.85641215688,0.0,1.48512980543,0.74256470291,0.371282391416,0.0,0.0,0.0,0.371282391416,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # 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=y13_THT_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"THT",\
                 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=(y13_THT_0_weights).max()*1.1
      ymin=0 # linear scale
      #ymin=min([x for x in (y13_THT_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_12.png')
      plt.savefig('../../PDF/MadAnalysis5job_0/selection_12.png')
      plt.savefig('../../DVI/MadAnalysis5job_0/selection_12.eps')
  
  # Running!
  if __name__ == '__main__':
      selection_12()
  