old_contents: #!/usr/bin/python
  # -*- coding: utf-8 -*-
  
  import argparse
  import matplotlib.pyplot as plt
  import pandas as pd
  
  parser = argparse.ArgumentParser(description='Plot data from output of the n-body simulation.')
  parser.add_argument('--output', type=str, default='output_int.dat',
                      help='The output file (default %(default)s)')
  args = parser.parse_args()
  
  
  if __name__ == '__main__':
      # Read data as CSV
      data = pd.read_csv(args.output, delim_whitespace=True)
  
      # Plot
      plt.plot(data.t, data.Ep, label='$Ep$')
      plt.plot(data.t, data.Ec, label='$Ec$')
      plt.plot(data.t, data.E, label='$E_\mathrm{tot}$')
  
      plt.grid('on')
      plt.legend()
      plt.show()
  
new_contents: #!/usr/bin/python
  # -*- coding: utf-8 -*-
  
  import argparse
  import matplotlib.pyplot as plt
  import pandas as pd
  
  parser = argparse.ArgumentParser(description='Plot data from output of the n-body simulation.')
  parser.add_argument('--output', type=str, default='output_int.dat',
                      help='The output file (default %(default)s)')
  args = parser.parse_args()
  
  
  if __name__ == '__main__':
      # Read data as CSV
      data = pd.read_csv(args.output, delim_whitespace=True)
  
      # Plot
      plt.plot(data.t, data.Ep.abs(), label='$Ep$')
      plt.plot(data.t, data.Ec.abs(), label='$Ec$')
      plt.plot(data.t, data.E.abs(), label='$E_\mathrm{tot}$')
  
      plt.yscale('log')
  
      plt.grid('on')
      plt.legend()
      plt.show()
  
subject: Use absolute value for E and logscale