new_contents: import numpy as np
  import matplotlib.pyplot as plt
  import atomic
  
  from ensemble_average import time_dependent_power
  
  
  if __name__ == '__main__':
      times = np.logspace(-7, 0, 50)
      temperature = np.logspace(0, 3, 50)
      density = 1e19
  
      from atomic.pec import TransitionPool
      ad = atomic.element('argon')
      tp = TransitionPool.from_adf15('adas_data/pec/*ar*.dat')
      ad = tp.filter_energy(2e3, 20e3, 'eV').create_atomic_data(ad)
  
      rt = atomic.RateEquations(ad)
      y = rt.solve(times, temperature, density)
  
      taus = np.array([ 1e14, 1e15, 1e16, 1e17, 1e18])/density
  
      plt.figure(1); plt.clf()
      from filter_construction import plot_coeffs
      plot_coeffs(ad, temperature, 5)
      plt.ylim(1e-35, 1e-30)
      plt.draw()
  
      plt.figure(2); plt.clf()
      time_dependent_power(y, taus)
      plt.draw()
  
      plt.figure(3); plt.clf()
      time_dependent_power(y, taus, ensemble_average=True)
      plt.draw()
  
  
      plt.show()
  
subject: Add example: filtered ensemble average.