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