At this point, you should have a computed all experimental G-vectors. You are now ready for indexing grains. This page shows you the indexing procedure with GrainSpotter. For an alternative way of indexing, check out Indexing with ImageD11.

GrainSpotter first generates a number of random grain orientations and, for each, calculates the corresponding theoretical G-vectors. For each grain orientation, GrainSpotter looks for a possible match between the theoretical G-vectors and those found in the experiment. If the convergence criteria are met (see details below), the grain is assigned.

For the indexation, uncertainties in 2θ, η and ω will be critical for assigning the experimental G-vectors to a grain. You can use Fabian to estimate uncertainties on ω and η and ImageD11 to estimate uncertainties in 2θ.

In Fabian, load your peaks from the peaksearch and overlap them with the diffraction data (CrystTools > Peaks > read). In ImageD11, display your peaks as 2θ/η plot. You can then evaluate the maximum δ2θ, δη, and δω ranges you can use to avoid mixing up peaks.

ImageD11 is not a good tool for evaluating δη and δω as all peaks extracted for all ω are stacked on the same plot. It is appropriate, however, to evaluate δ2θ.

Result of a peak search displayed in Fabian. Red circles are peaks. Using Fabian you can estimate the maximum δη interval (azimuthal coordinate on the detector plane) that can be used without confusing peaks. By moving from one image to the next, you can also estimate the maximum value of δω interval that can be used while avoiding confusion between peaks.

2θ/η plot in ImageD11. This can be used to assign a maximum δ2θ interval that will avoid confusion between the diffraction lines. Pay attention to this step. If your 2θ uncertainty is too large, the indexing will be completely wrong.

You are now ready to start indexing your g-vectors with GrainSpotter.

GrainSpotter works with an input file, typically ending with a .ini extension in which you should define

• the crystal structure of the grains you are looking for,
• the 2θ, η and ω ranges in which to look for g-vectors, the 2θ can also be expressed in d-spacing range,
• the cuts, above which a grain is considered a grain,
• the uncertainties in 2θ, η and ω,
• a nσ factor that will be applied to the uncertainties above,
• the number of random tries.

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

You should play on the cut and uncertainties to optimize the parameters that will lead to the best results.

When your input file is ready, type either

 GrainSpotter.0.90 index.ini

or

 GrainSpotter index.ini

or

 grainspotter index.ini

Below is a sample GrainSpotter input file that we actually used:

• lines started with ! are commented out and will not be used,
• we define several 2theta ranges in which we actually look for peaks (other changes were polluted by an additional phase,
• images were acquired with ω in the [-28°;+28] range in steps of 0.5°,
• the GVE file to start from is peaks-I-Want-To-Index.gve,
• results will be saved in grains-I-found.log
• cuts are as follow
  • 15 peaks, minimum per grain,
  • 30% completeness minimum, which is quite low but diamond anvil cells have shadows, peaks may be hidden by the pressure medium, etc. If the completeness restriction is too high, you will not find enough grains,
  • 50% uniqueness: has no effect in the experiments we performed,
• uncertainties: 0.02° in 2θ, 1° in η and 2° in ω, which are all multiplied by a nσ of 2 (line below)
• tries for 10000 random orientations and stops,

spacegroup 62               		! spacegroup [space group nr]
! dsrange 0 0.34                         ! dsrange [min max], d-spacing range, multiple ranges can be specified
tthrange 3.0 7.15                          ! tthrange [min max], multiple ranges can be specified
tthrange 7.35 10.2                          ! tthrange [min max], multiple ranges can be specified
tthrange 10.3 12.5                          ! tthrange [min max], multiple ranges can be specified
tthrange 12.65 14.5                          ! tthrange [min max], multiple ranges can be specified
etarange 0 360               		! etarange [min max], multiple ranges can be specified
domega 0.5                       	! domega [stepsize] in omega, degrees
omegarange -28 28 		! omegarange [min max] degrees, multiple ranges can be specified
filespecs peaks-I-Want-To-Index.gve grains-I-found.log	! filespecs [gvecsfile grainsfile]
cuts 15 0.3 0.5               		! cuts [min_measuments min_completeness min_uniqueness]
eulerstep 5               		! eulerstep [stepsize] : angle step size in Euler space
uncertainties 0.02 1 2    		! uncertainties [sigma_tth sigma_eta sigma_omega] in degrees
nsigmas 2                   		! nsigmas [Nsig] : maximal deviation in sigmas
! minfracg 1                            ! stop search when minfracg (0..1) of the gvectors have been assigned to grains
! Nhkls_in_indexing 15			! Nhkls_in_indexing [Nfamilies] : use first Nfamilies in indexing
random 100000                           ! use randomly chosen orientation seeds #trials
! positionfit                            ! fit the position of the grain
! genhkl                                  ! generate list of hkl's based on space group and cell parameters in gve file

It can be efficients to run multiple loops of grainspotter indexings. The underlying concept is as follow

• Step 1:
  • run grainspotter with a given set of specifications, remove all indexed peaks from the GVE file,
  • repeat the above operation X times,
  • this will provide a first set of grains, which should be the most reliable
• Step 2:
  • Lower the tolerance, and repeat X loops of indexings, remove indexed peaks at each step
  • this will provide a second set of grains, which may have to be checked
• Repeat steps above, lowering tolerances progressively in order to optimize the number of indexed grains, while making sure that all indexed grains make sense
• Combined the results of all indexings into one main log file, with all indexed files from the loop.