text: def selection_14():
  
      # 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(0.0,8000.0,81,endpoint=True)
  
      # Creating data sequence: middle of each bin
      xData = numpy.array([50.0,150.0,250.0,350.0,450.0,550.0,650.0,750.0,850.0,950.0,1050.0,1150.0,1250.0,1350.0,1450.0,1550.0,1650.0,1750.0,1850.0,1950.0,2050.0,2150.0,2250.0,2350.0,2450.0,2550.0,2650.0,2750.0,2850.0,2950.0,3050.0,3150.0,3250.0,3350.0,3450.0,3550.0,3650.0,3750.0,3850.0,3950.0,4050.0,4150.0,4250.0,4350.0,4450.0,4550.0,4650.0,4750.0,4850.0,4950.0,5050.0,5150.0,5250.0,5350.0,5450.0,5550.0,5650.0,5750.0,5850.0,5950.0,6050.0,6150.0,6250.0,6350.0,6450.0,6550.0,6650.0,6750.0,6850.0,6950.0,7050.0,7150.0,7250.0,7350.0,7450.0,7550.0,7650.0,7750.0,7850.0,7950.0])
  
      # Creating weights for histo: y15_TET_0
      y15_TET_0_weights = numpy.array([2.59177698141,44.835417609,78.8052576906,102.497138301,118.590078117,127.434609296,133.165924022,135.701715793,131.077201662,126.045871956,118.073413941,111.035348585,103.481378559,95.7545209078,88.0667978198,79.8206377079,73.8471204101,67.4746624182,60.6083654147,55.4430028278,50.282636993,45.103403422,40.9942343241,36.9641298354,33.0658796432,30.0335905884,26.2600313489,23.8937733833,21.415927948,19.1623487428,16.9678231535,15.2722132761,13.1531906111,11.8795824385,10.4841534477,9.51578287494,8.08543258251,7.00323999248,6.38331893245,5.48474299418,4.88132320855,4.1935422565,3.82665153031,3.31611377268,2.76342581208,2.39195046583,2.11540941363,1.90169472517,1.61382863408,1.3817766646,1.19583992087,1.04951582947,0.90321452326,0.72218179199,0.631596671045,0.531724786366,0.455095394393,0.408684760652,0.332060965042,0.260059924706,0.255451719974,0.195039427115,0.21129286261,0.127691402384,0.130017530433,0.0975111391307,0.071966302915,0.067343627589,0.0557406493617,0.0719811732495,0.0371689447086,0.0185728119334,0.0185712289623,0.0255566965066,0.0139450439175,0.00928174699497,0.0139186570686,0.00929156461365,0.00930003510795,0.0162422987342])
  
      # 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=y15_TET_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"TET",\
                 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=(y15_TET_0_weights).max()*1.1
      ymin=0 # linear scale
      #ymin=min([x for x in (y15_TET_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_14.png')
      plt.savefig('../../PDF/MadAnalysis5job_0/selection_14.png')
      plt.savefig('../../DVI/MadAnalysis5job_0/selection_14.eps')
  
  # Running!
  if __name__ == '__main__':
      selection_14()
  