If your dataset is complex, here is a list of tricks to get it indexed. This page is being improved over time.

 from ImageD11 import columnfile 
 c = columnfile.columnfile('peaks_t100.flt') % if 'expection: problem interpreting your colfile' check the extention of your file. Should be .flt 
 c.titles % to see what variables you have in your columnfile
 c.parameters.loadparameters("CeO2_parameters.prm")
 c.titles
 c.updateGeometry()
  import pylab
  pylab.figure()
  pylab.show()
  import matplotlib
  matplotlib.use("GTK3Agg")
 from pylab import *
 show()
 from ImageD11.columnfile import *
  c = columnfile("peaks_t100.flt")
  c.parameters.loadparameters("CeO2_parameters.prm")
  c.updateGeometry()
  tth=arange(0,15,.01)
 plot(np.histogram(c.tth,bins=tth)[0],"-")
  show()
  plot(tth[1:],np.histogram(c.tth,bins=tth)[0],"-")  % give a better peaks separation
  show()
 plot(tth[1:],np.histogram(c.tth,bins=tth,weights=c.sum_intensity)[0],"-")  % take in account the intensity of the peaks

% open a python console

 python

 import matplotlib
 matplotlib.use("GTK3Agg")
 from pylab import *
 from ImageD11.columnfile import *
 c = columnfile('peaks_t100.flt')
 c.parameters.loadparameters("CeO2_parameters.prm")
 c.updateGeometry()
 tth = arange(0,15,.01)
 plot(tth[1:],np.histogram(c.tth,bins=tth)[0],"-")
 show()

Trick 2: work with large grain first