new_contents: from matplotlib import pyplot as plt
  import pandas as pd
  import seaborn as sns
  sns.set_style('white')
  
  df = pd.read_table('eiee.15.bed.gz', compression='gzip')
  print(df.head())
  cols = list(df.columns)
  cols[0] = "chrom"
  cols = [c for c in cols[3:] if c != '15-0022964']
  
  
  fig, ax = plt.subplots(1)
  
  for c in cols:
      ax.plot(df['start'], df[c], color='#dddddd', lw=0.4)
  
  ax.plot(df['start'], df['15-0022964'], color='#487535', lw=0.4)
  ax.set_ylabel("Scaled Coverage")
  ax.set_xlabel("Position On Chromosome 15")
  ax.set_xlim(xmin=0, xmax=df.start.max())
  print(df.start.max())
  ax.set_ylim(0, 6)
  plt.draw()
  ticks = ax.get_xticks()
  
  labels = ["%dM" % (t / 1000000) for t in ticks if t < df.start.max()]
  ax.set_xticks(ticks, labels)
  ax.set_xticklabels(labels)
  ax.set_xlim(xmin=0, xmax=df.start.max())
  sns.despine()
  
  plt.show()
  
  plt.close()
  
  
  fig, ax = plt.subplots(1)
  
  df = pd.read_table('eiee.15.roc')
  for c in cols:
      ax.plot(df['cov'], df[c], color='#dddddd', lw=2)
  
  ax.plot(df['cov'], df['15-0022964'], color='#487535', lw=2)
  ax.set_xlabel("Scaled Coverage")
  ax.set_ylabel("Proportion of Regions Covered")
  sns.despine()
  plt.show()
  
subject: add script to plot eiee figure (2)