text: #!/Library/Frameworks/Python.framework/Versions/3.8/bin/python3
  """
  """
  
  
  from matplotlib import pyplot as plt
  import json
  import numpy as np
  from scipy.optimize import curve_fit
  import sys
  
  from BACs_ccs_vs_mz import compaction_factor
  
  
  fset = sys.argv[1]
  
  # set up plot fonts
  from matplotlib import rcParams
  rcParams['font.family'] = 'sans-serif'
  rcParams['font.sans-serif'] = ['Helvetica', 'Arial']
  rcParams['font.size'] = 11
  
  # compute weighted average of predicted CCS
  a = np.loadtxt('TERF_y_{}.txt'.format(fset))
  b = np.loadtxt('TERF_cluster_sizewt.txt', usecols=(1))
  c = np.loadtxt('TERFO_y_{}.txt'.format(fset))
  d = np.loadtxt('TERFO_cluster_sizewt.txt', usecols=(1))
  wavg = sum(a * b) / sum(b)
  wavgO = sum(c * d) / sum(d)
  
  fig = plt.figure(figsize=(3, 4))
  ax = fig.add_subplot(111)
  
  # common settings for the same style across plots
  bs = {
      'linewidth': 1.1, 'align': 'center', 'width': 0.8, 'capstyle': 'round', 'capsize': 5, 
      'error_kw': {
          'elinewidth': 1.1, 'ecolor': 'k'
      }
  }
  
  c = 'k'
  ax.bar([1], [227.04], edgecolor=c, color=c, fill=False, hatch='///', **bs)
  ax.bar([1], [227.04], edgecolor=c, ecolor=c, fill=False, **bs)
  c = 'b'
  ax.bar([2], [222.82], edgecolor=c, color=c, fill=False, hatch='///', **bs)
  ax.bar([2], [222.82], edgecolor=c, ecolor=c, fill=False, **bs)
  ax.text(1.7, 225, '{:.3f}'.format(compaction_factor(472.3376, 227.04, 488.3268, 222.82)), fontsize=10, c='b')
  c = 'k'
  ax.bar([3], [wavg], edgecolor=c, color=c, **bs)
  ax.bar([3], [wavg], edgecolor=c, ecolor=c, fill=False, **bs)
  c = 'b'
  ax.bar([4], [wavgO], edgecolor=c, color=c, **bs)
  ax.bar([4], [wavgO], ecolor=c, edgecolor=c, fill=False, **bs)
  ax.text(3.7, wavgO + 2, '{:.3f}'.format(compaction_factor(472.3376, wavg, 488.3268, wavgO)), fontsize=10, c='b')
  
  ax.set_xticks([1, 2, 3, 4])
  ax.set_xticklabels(['TERF (exp.)', 'TERF+O (exp.)', 'TERF (pred.)', 'TERF+O (pred.)'], fontstyle='italic', fontsize=10, rotation='vertical')
  
  
  ax.set_ylim([200, 245])
  
  
  for d in ['top', 'right']:
      ax.spines[d].set_visible(False)
  ax.set_ylabel(r'CCS ($\AA^2$)')
  
  
  plt.tight_layout()
  
  # save figure
  png = 'terf_{}_modeling_ccscomp.png'.format(fset)
  plt.savefig(png, dpi=400, bbox_inches='tight')
  
  #plt.show()
  plt.close()
  
  
  
  
  
  