ImageD11 can also do indexing, using a completely different philosophy from that of GrainSpotter.

The idea is based on the following:

• Select 2 diffraction rings from your sample
• For each pair of peaks (one on each ring)
  • Test whether a grain could have those 2 peaks
  • If so, calculate the corresponding grain orientation.
• For each test orientation, generated above, look for for more peaks that could be assigned to this grain. If a given threshold is reached, accept the grain as a true grain.

First, get a gve file from ImageD11 as seen before.

Then go in ImageD11 > Indexing > load g-vectors and choose your gve file. Use Assign peaks to powder rings. This command will show all the theoretical peaks of the unit cell (for the phase you define in transformation > parameters) and the number of measured peaks it assigns at each theoretical peaks.

Choose 2 peaks in the list : better peaks have low multiplicity and a lots of peaks expected. Use the indexing > edit parameters menu and enter the index of these rings in ring_1 and ring_2. In hit indexing > generate trial orientation then indexing > score trial orientation. At the end it give you the number of grains indexed.

in that script, you need to load your gve file (from ImageD11), your parameters file (from ImageD11) and your flt file (from peaksearch). Don't forget to specify the name of the ouput file. In the line myindexer.parameterobject.set_parameters, you can set the indexing parameters (mainly minpks and hkl_tol). To specify the rings you want to use for the indexation, copy the list of peaks from Assign peaks to powder rings in the bloc below rings = [] and enter in that rings = [] list the index of the rings you want to use. This script will pair every ring with all the other to index the peaks in grains. At the end, it will give you the number of grains it found. Once it is finish, the indexed grains are stored in a ubi file and you can get back the not indexed grains to run idx.py again with more generous parameters and try to found more grains with the left peaks.

To have a look at what you indexed, you can plot different things : (command for plot were written in a Xterm terminal)

-the number of peaks in grains vs the error : go in ImageD11 indexing > histogram fit quality

-the number of peaks in a grain vs the intensity : from ImageD11.columnfile import * c = columnfile(“peaks_t0.flt.new”) c.parameters.loadparameters(“parameters.prm”) c.updateGeometry() clf() c.filter(c.tth<13) % we only keep the rings below 13° cause after it's too low intensity plot(c.tth[~(c.labels>=0)], log(c.sum_intensity[~(c.labels>=0)]),“,”) or : plot(c.labels, log(c.sum_intensity),“,”) to save it : c.writefile(“t0la.flt”)

-plot tth vs intensity : plot(d.tth, d.sum_intensity,“o”,ms=5) (with d the variable containing the peaks)

-??

plotgrainhist.py peaks_t0.flt parameters.prm t0.map .05 10 .25

We run into the problem that in simulated datasets, a large number of peaks have an intensity below 1 and when saving the images, these intensities are rounded at 0. Consequently, these peaks are not detected anymore by the software. To resolve this issue, we want to consider only peaks with large intensity. So, to select the rings we want to use :

python pickrings.py ../Simulation/simu_FoCIF_omega-28-28_1000grains.flt parameters.prm fewr_ideal.flt

After that, run again the indexation (idx_0.py) with your new flt file.

For now, we still miss grains…