OptimizeLatticeForCellType v1#

Summary#

Optimize lattice parameters for cell type.

Properties#

Name	Direction	Type	Default	Description
PeaksWorkspace	InOut	IPeaksWorkspace	Mandatory	An input PeaksWorkspace with an instrument.
CellType	Input	string	Cubic	Select the cell type. Allowed values: [‘Cubic’, ‘Tetragonal’, ‘Orthorhombic’, ‘Hexagonal’, ‘Rhombohedral’, ‘Monoclinic’, ‘Triclinic’]
Apply	Input	boolean	False	Re-index the peaks
PerRun	Input	boolean	False	Make per run orientation matrices
Tolerance	Input	number	0.12	Indexing Tolerance
EdgePixels	Input	number	0	Remove peaks that are at pixels this close to edge.
OutputChi2	Output	number		Returns the goodness of the fit
OutputDirectory	Input	string	.	The directory where the per run peaks files and orientation matrices will be written.

Description#

This does a least squares fit between indexed peaks and Q values for a set of runs producing an overall leastSquare orientation matrix.

Get estimates of the standard deviations of the parameters, by approximating chisq by a quadratic polynomial through three points and finding the change in the parameter that would cause a change of 1 in chisq. (See Bevington, 2nd ed., pg 147, eqn: 8.13 ) In this version, we calculate a sequence of approximations for each parameter, with delta ranging over 10 orders of magnitude and keep the value in the sequence with the smallest relative change.

Usage#

Example:

ws=LoadIsawPeaks("TOPAZ_3007.peaks")
FindUBUsingFFT(ws,MinD=8.0,MaxD=13.0)
print("Before Optimization:")
print(ws.sample().getOrientedLattice().getUB())
OptimizeLatticeForCellType(ws,CellType="Monoclinic")
print("\nAfter Optimization:")
print(ws.sample().getOrientedLattice().getUB())

Output:

Before Optimization:
[[ 0.01223576  0.00480107  0.08604016]
 [-0.11654506  0.00178069 -0.00458823]
 [-0.02737294 -0.08973552 -0.02525994]]

After Optimization:
[[-0.04519948  0.04084577 -0.01259083]
 [ 0.00160373 -0.00322317  0.11582993]
 [ 0.05743318  0.03223531  0.02753568]]

Categories: AlgorithmIndex | Crystal\Cell