old_contents: #!/bin/env python
  
  import numpy as np
  import scipy.signal as ss
  import astropy.io.fits as fits
  import matplotlib.pyplot as plt
  
  inpt = str(raw_input("Nome do Arquivo: "))
  lc = fits.open(inpt)
  bin = float(raw_input("bin size (or camera resolution): "))
  
  # Convert to big-endian array is necessary to the lombscargle function
  rate = np.array(lc[1].data["RATE"], dtype='float64')
  time = np.array(lc[1].data["TIME"], dtype='float64')
  time -= time.min()
  
  # Exclue NaN values -------------------------
  print ''
  print 'Excluding nan and negative values...'
  print ''
  
  exclude = []
  for i in xrange(len(rate)):
      if rate[i] > 0:
          pass
      else:
          exclude.append(i)
  
  exclude = np.array(exclude)
  nrate = np.delete(rate, exclude)
  ntime = np.delete(time, exclude)
  # --------------------------------------------
  
  # normalize count rate
  nrate -= nrate.mean()
  
  # sample frequecy limited by resolution
  fsample = 1.0/(bin)
  
  numb = 10*len(time)
  
  # Thr periodogram itself
  f, p = ss.periodogram(nrate, fs=fsample, nfft=numb)
  
  # normalize the periodogram to unts of nrate/freq
  pnorm = np.sqrt(p)/(2*len(time))
  
  print 'f_max = ', max(f)
  print 'f_min = ', min(f)
  print 'T_obs =', max(time)
  
  # # Plot lightcurve on top panel
  # plt.subplot(2, 1, 1)
  # plt.plot(ntime, nrate, 'bo-')
  # plt.xlabel('Tempo (s)', fontsize=12)
  # plt.ylabel('Cts. s$^{{-1}}$', fontsize=12)
  #
  # # Plot powerspectrum on bottom panel
  # plt.subplot(2, 1, 2)
  plt.plot(f, pnorm, 'b-',
      label='T$_{{pico}}$ = {0:.0f} s'.format(1/f[np.argmax(pnorm)]))
  plt.xlabel('Frequencia (Hz)', fontsize=12)
  plt.ylabel('Potencia', fontsize=12)
  plt.xlim(min(f), max(f))
  plt.legend(loc=1)
  
  # save and show plot
  plt.savefig("periodogram_testes.pdf", orientation='landscape', papertype='a4',
          format='pdf', bbox_inches='tight')
  plt.show()
  
new_contents: #!/bin/env python
  
  import numpy as np
  import scipy.signal as ss
  import astropy.io.fits as fits
  import matplotlib.pyplot as plt
  
  inpt = str(raw_input("Nome do Arquivo: "))
  lc = fits.open(inpt)
  bin = float(raw_input("bin size (or camera resolution): "))
  
  # Convert to big-endian array is necessary to the lombscargle function
  rate = np.array(lc[1].data["RATE"], dtype='float64')
  time = np.array(lc[1].data["TIME"], dtype='float64')
  time -= time.min()
  
  # Exclue NaN values -------------------------
  print ''
  print 'Excluding nan and negative values...'
  print ''
  
  exclude = []
  for i in xrange(len(rate)):
      if rate[i] > 0:
          pass
      else:
          exclude.append(i)
  
  exclude = np.array(exclude)
  nrate = np.delete(rate, exclude)
  ntime = np.delete(time, exclude)
  # --------------------------------------------
  
  # normalize count rate
  nrate -= nrate.mean()
  
  # sample frequecy limited by resolution
  fsample = 1.0/(bin)
  
  numb = 10*len(time)
  
  # Thr periodogram itself
  f, p = ss.periodogram(nrate, fs=fsample, nfft=numb, scaling='spectrum')
  
  # normalize the periodogram to unts of nrate/freq
  pnorm = np.sqrt(p*2.0)
  
  print 'f_max = ', max(f)
  print 'f_min = ', min(f)
  print 'T_obs =', max(time)
  
  # # Plot lightcurve on top panel
  # plt.subplot(2, 1, 1)
  # plt.plot(ntime, nrate, 'bo-')
  # plt.xlabel('Tempo (s)', fontsize=12)
  # plt.ylabel('Cts. s$^{{-1}}$', fontsize=12)
  #
  # # Plot powerspectrum on bottom panel
  # plt.subplot(2, 1, 2)
  plt.plot(f, pnorm, 'b-',
      label='T$_{{pico}}$ = {0:.0f} s'.format(1/f[np.argmax(pnorm)]))
  plt.xlabel('Frequencia (Hz)', fontsize=12)
  plt.ylabel('Potencia', fontsize=12)
  plt.xlim(min(f), max(f))
  plt.legend(loc=1)
  
  # save and show plot
  plt.savefig("periodogram_testes.pdf", orientation='landscape', papertype='a4',
          format='pdf', bbox_inches='tight')
  plt.show()
  
subject: fix normalization