OptimizeCrystalPlacementByRun v1#
Summary#
Optimizes the sample position for each run in a peaks workspace.
See Also#
Properties#
Name |
Direction |
Type |
Default |
Description |
|---|---|---|---|---|
InputWorkspace |
Input |
Mandatory |
The name of the peaks workspace that will be optimized. |
|
Tolerance |
Input |
number |
0.15 |
Tolerance of indexing of peaks. |
OutputWorkspace |
Input |
string |
The name of the peaks workspace that will be created. |
Description#
This algorithm basically optimizes h,k, and l offsets from an integer by varying the parameters sample positions for each run in the peaks workspace.
The crystal orientation matrix, UB matrix, from the PeaksWorkspace should index all the runs “very well”. Otherwise iterations that slowly build a UB matrix with corrected sample orientations may be needed.
Usage#
Example:
ws=LoadIsawPeaks("calibrated.peaks")
FindUBUsingFFT(PeaksWorkspace=ws,MinD=2,MaxD=20,Tolerance=0.12)
IndexPeaks(PeaksWorkspace='ws',Tolerance=0.12)
wsd = OptimizeCrystalPlacementByRun(InputWorkspace=ws,OutputWorkspace='wsd',Tolerance=0.12)
print('Optimized %s sample position: %s'%(mtd['wsd'].getPeak(0).getRunNumber(),mtd['wsd'].getPeak(0).getSamplePos()))
print('Optimized %s sample position: %s'%(mtd['wsd'].getPeak(8).getRunNumber(),mtd['wsd'].getPeak(8).getSamplePos()))
Output:
Optimized 71907 sample position: [-0.000678629,-2.16033e-05,0.00493278]
Optimized 72007 sample position: [-0.0027929,-0.00105681,0.00497094]
Categories: AlgorithmIndex | Crystal\Corrections
Source#
Python: OptimizeCrystalPlacementByRun.py