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

Trick 1: plot intensity vs. 2theta from peak histogram in ImageD11:

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