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FindUBUsingIndexedPeaks dialog.
Table of Contents
| Name | Direction | Type | Default | Description |
|---|---|---|---|---|
| PeaksWorkspace | InOut | IPeaksWorkspace | Mandatory | Input Peaks Workspace |
| Tolerance | Input | number | 0.1 | Indexing Tolerance (0.1) |
| ToleranceForSatellite | Input | number | 0.1 | Indexing Tolerance for satellite (0.1) |
| CommonUBForAll | Input | boolean | False | Used when evaluating the uncertainty of modHKL |
Given a set of peaks at least three of which have been assigned Miller indices, this algorithm will find the UB matrix, that best maps the integer (h,k,l) values to the corresponding Q vectors. The set of indexed peaks must include three linearly independent Q vectors. The (h,k,l) values from the peaks are first rounded to form integer (h,k,l) values. The algorithm then forms a possibly over-determined linear system of equations representing the mapping from (h,k,l) to Q for each indexed peak. The system of linear equations is then solved in the least squares sense, using QR factorization.
The option CommonUBForAll only effects the calculation of the
uncertainty in the modulation UB. If True the UB will be optimized
separately for each subset of runs before calculating the error.
Example:
ws=LoadIsawPeaks("TOPAZ_3007.peaks")
print("After LoadIsawPeaks does the workspace have an orientedLattice: %s" % ws.sample().hasOrientedLattice())
FindUBUsingIndexedPeaks(ws)
print("After FindUBUsingIndexedPeaks does the workspace have an orientedLattice: %s" % ws.sample().hasOrientedLattice())
print(ws.sample().getOrientedLattice().getUB())
Output:
After LoadIsawPeaks does the workspace have an orientedLattice: False
After FindUBUsingIndexedPeaks does the workspace have an orientedLattice: True
[[-0.04542062 0.04061954 -0.01223576]
[ 0.00140377 -0.00318446 0.11654506]
[ 0.05749773 0.03223779 0.02737294]]
Categories: AlgorithmIndex | Crystal\UBMatrix