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processing:indexing_with_grainspotter

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Indexing

When you have filtered your peaks with peaksearch, you can index them.

Estimation of the uncertainties

For the indexation, the uncertainties in 2θ, η and ω will be critical for finding the right grains.

You can use

fabian.py

to estimate uncertainties on ω and η.

To overlap the peaks kept by peaksearch on the diffraction shots, use 'CrystTools' > 'Peaks' > 'read' and choose the filtered peaks file.

Plot of peaks for 500 simulated fosterite grains. The red circles correspond to the peaks selected by peaksearch for a treshold of 2.

uncertainty on η : maximal error on η (azimuthal coordinate on the detector plane) the software can use without confusing peaks;

uncertainty on ω : number of diffraction shots separating two peaks on the same position (multiplied by the rotation step of the diamond anvil cell);

To estimate uncertainty on 2θ, you can use ImageD11 later in the process.

From diffraction peaks to G_vectors

ImageD11 will allow you to obtain a .gve file with the G-vectors of the peaks you find with peaksearch.

  • open the software with the command :

ImageD11_gui.py

  • in the menu 'transformation' click on 'load filtered peaks' and choose the file you obtained with peacksearch;
  • in the menu 'transformation' use 'plot y/z';

Plot x/y in ImageD11 for 500 simulated fosterite grains with a peaksearch treshold of 2. Tip : if you obtain ellipses instead of circles, try to resize the ImageD11 window.

  • enter the parameters of the experiment ('transformation' > 'edit parameters')
  • you can save the parameters : 'transformation' > 'save parameters' (end the name of the file by .prm);
  • clear the plot and use 'transformation' > 'plot tth/eta';

tth/eta plot for the same set of grains.

Zoom on peaks positions between 2θ=5 and 2θ=11(again, same set of data). Unit cell peaks positions in red.

  • here you can estimate the uncertainty on 2θ : determine the maximal width of the peak line for which the spots of each line do not mix.
  • compute and save G-vectors : 'transformation' > 'compute g-vectors' then 'transformation' > 'save g-vectors' (do not forget to end the name of the file with .gve)
  • in the menu 'Indexing' use 'load g-vectors' and choose your gve file.
  • then use 'Indexing' > 'Assign peaks to powder rings' to see in the command windows the hkl plan associated to each Debye Ring of your diffraction data and the number of mesurements found for each of them.

With that G-vectors file, you will be able to use GrainSpotter to find your original grains.

From G-vectors to grains

You can now index your diffraction peaks with GrainSpotter.

But before entering the commande line in the Konsole, you must complete the input file : index.ini. In that file you must write the same parameters you used in your experiment/simulation and in ImageD11, but you can vary :

  • the 2θ range;
  • the cut
  • the uncertainties
  • the random number

Changing the 2θ range allow you to exclude some domains where the peaks are not well defined.

You should play on the cut and the uncertainties to found the settings with the best indexation. Increasing the minimum of measurements allow to decrease the number of erroneous indexed grains but it limite the number of indexed grains found. The uniqueness and the completness do not have a great importance on the indexation (I set both to zero in my best indexation scenario). For defining the incertainties, use the plot from fabian and ImageD11.

Increasing random number increase the number of grains indexed but slow a bit the calculation.

When your input file is ready, type :

GrainSpotter.0.90 index.ini

nb : the commande GrainSpotter instead of GrainSpotter.0.90 work too, I do not know what is the difference.

processing/indexing_with_grainspotter.1550699799.txt.gz · Last modified: by smerkel