4.2

GetDetectorOffsets v1

../_images/GetDetectorOffsets-v1_dlg.png

GetDetectorOffsets dialog.

Summary

Creates an OffsetsWorkspace containing offsets for each detector. You can then save these to a .cal file using SaveCalFile.

Properties

Name Direction Type Default Description
InputWorkspace Input MatrixWorkspace Mandatory A 2D workspace with X values of d-spacing
Step Input number 0.001 Step size used to bin d-spacing data
DReference Input number 2 Center of reference peak in d-space
XMin Input number 0 Minimum of CrossCorrelation data to search for peak, usually negative
XMax Input number 0 Maximum of CrossCorrelation data to search for peak, usually positive
GroupingFileName Input string   Optional: The name of the output CalFile to save the generated OffsetsWorkspace. Allowed extensions: [‘.cal’]
OutputWorkspace Output OffsetsWorkspace   An output workspace containing the offsets.
MaskWorkspace Output MatrixWorkspace Mask An output workspace containing the mask.
PeakFunction Input string Gaussian The function type for fitting the peaks. Allowed values: [‘BackToBackExponential’, ‘Bk2BkExpConvPV’, ‘DeltaFunction’, ‘ElasticDiffRotDiscreteCircle’, ‘ElasticDiffSphere’, ‘ElasticIsoRotDiff’, ‘ExamplePeakFunction’, ‘Gaussian’, ‘IkedaCarpenterPV’, ‘Lorentzian’, ‘PseudoVoigt’, ‘Voigt’]
MaxOffset Input number 1 Maximum absolute value of offsets; default is 1
OffsetMode Input string Relative Whether to calculate a relative or absolute offset. Allowed values: [‘Relative’, ‘Absolute’]
DIdeal Input number 2 The known peak centre value from the NIST standard information, this is only used in Absolute OffsetMode.

Description

This algorithm requires a workspace that is both in d-spacing, but has also been preprocessed by the CrossCorrelate v1 algorithm. In this first step you select one spectrum to be the reference spectrum and all of the other spectrum are cross correlated against it. Each output spectrum then contains a peak whose location defines the offset from the reference spectrum.

The algorithm iterates over each spectrum in the workspace and fits a PeakFunction (default is a Gaussian function) to the reference peaks. The fit is used to calculate the centre of the fitted peak, and the offset is then calculated as:

-peakCentre*step/(dreference+PeakCentre*step)

This is then written into a .cal file for every detector that contributes to that spectrum. All of the entries in the cal file are initially set to both be included, but also to all group into a single group on DiffractionFocussing v2. The CreateCalFileByNames v1 algorithm can be used to alter the grouping in the cal file.

Usage

import os

# Create a workspace with a Gaussian peak in the centre.
ws = CreateSampleWorkspace(Function='User Defined',UserDefinedFunction='name=Gaussian,Height=1,PeakCentre=10,Sigma=1',XMin=0,XMax=20,BinWidth=0.1)
ws.getAxis(0).setUnit( 'dSpacing' )

# Generate a file path to save the .cal file at.
calFilePath = os.path.expanduser( '~/MantidUsageExample_CalFile.cal' )

# Run the algorithm
msk = GetDetectorOffsets(ws,0.001,10.0,0, 10, calFilePath)

# Read the saved .cal file back in
f = open( calFilePath, 'r' )
file = f.read().split('\n')
f.close()

# Print out first 10 lines of the file
print("{} ...".format(file[0][:55]))
for line in file[1:10]:
    print(line)

Output

# Calibration file for instrument basic_rect written on ...
# Format: number    UDET         offset    select    group
        0            100     -0.0014265       1       1
        1            101     -0.0014265       1       1
        2            102     -0.0014265       1       1
        3            103     -0.0014265       1       1
        4            104     -0.0014265       1       1
        5            105     -0.0014265       1       1
        6            106     -0.0014265       1       1
        7            107     -0.0014265       1       1

Categories: AlgorithmIndex | Diffraction\Calibration

Source

C++ source: GetDetectorOffsets.cpp (last modified: 2019-06-05)

C++ header: GetDetectorOffsets.h (last modified: 2018-10-05)