FindUBUsingIndexedPeaks dialog.
Table of Contents
| Name | Direction | Type | Default | Description |
|---|---|---|---|---|
| PeaksWorkspace | InOut | PeaksWorkspace | Mandatory | Input Peaks Workspace |
| Tolerance | Input | number | 0.1 | Indexing Tolerance (0.1) |
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.
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: Algorithm Index | Crystal\UBMatrix
C++ source: FindUBUsingIndexedPeaks.cpp (last modified: 2018-10-05)
C++ header: FindUBUsingIndexedPeaks.h (last modified: 2018-10-05)