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SymmetriseMDHisto v1

Summary

Symmetrise MDHistoWorkspace using symmetry operations of Laue class point group.

Properties

Name	Direction	Type	Default	Description
InputWorkspace	Input	IMDHistoWorkspace	Mandatory	Input MDHistoWorkspace to symmetrise.
OutputWorkspace	Output	IMDHistoWorkspace	Mandatory	Symmetrised MDHistoWorkspace.
Pointgroup	Input	string		Point group Hermann–Mauguin symbol used to determine the point group of the Laue class.
Spacegroup	Input	string		Spacegroup Hermann–Mauguin symbol used to determine the point group of the Laue class.
WeightedAverage	Input	boolean	False	Perform average weighted by the error squared.

Description

Function to symmeterise an MDHistoWorkspace with 3 dimensions (HKL) using the symmetry operations of the point group of the Laue class (inferred from the PointGroup or Spacegroup provided).

Two types of average can be performed:

1. WeightedAverage=False (simple mean of the signal values and errors are added in quadrature then divided by the number of contributing bins)

2. WeightedAverage=True (weighted average performed using inverse square of errors as weights).

If (2) WeightedAverage=True then the signal is given by \(s = \Sigma_i w_i s_i\) where \(s_i\) is the signal in symmetrically equivalent bin \(i\) and the weights are the inverse of the error squared in each bin, \(w_i = 1/e_i^2\). The final error is given by \(1/e^2 = \Sigma_i w_i\).

Usage

Example:

from mantid.simpleapi import *

y = np.r_[4*[1], 4*[0]]
nbins = int(len(y) ** (1 / 3))
ws = CreateMDHistoWorkspace(SignalInput=y, ErrorInput=y,
                                 Dimensionality=3, Extents='-1,1,-1,1,-1,1',
                                 NumberOfBins=3 * [nbins], Names='H,K,L',
                                 Units='rlu,rlu,rlu')

ws_sym = SymmetriseMDHisto(ws, PointGroup="-1", WeightedAverage=False)

print("All HKL voxels have non-zero intensity?", np.all(ws_sym.getSignalArray()))

Output:

All HKL voxels have non-zero intensity? True

Categories: AlgorithmIndex | Crystal\DataHandling

Source

Python: SymmetriseMDHisto.py