ConvertCWSDExpToMomentum v1¶

ConvertCWSDExpToMomentum dialog.

Table of Contents

Summary
Properties
Description
Combine Experiment Into One MDEventWorkspace
Use cases
Usage
Source

Summary ¶

Load and convert a set of files in an HB3A experiment.

Properties ¶

Name	Direction	Type	Default	Description
InputWorkspace	Input	TableWorkspace	Mandatory	Name of table workspace for data file names in the experiment.
InstrumentFilename	Input	string		Allowed extensions: [‘.xml’]
DetectorSampleDistanceShift	Input	number	0	Amount of shift in sample-detector distance from 0.3750 meter.
DetectorCenterXShift	Input	number	0	Amount of shift of detector center in X-direction from (115, 128).
DetectorCenterYShift	Input	number	0	Amount of shift of detector center in Y-direction from (115, 128).
UserDefinedWavelength	Input	number	Optional	User defined wave length if it is specified.
CreateVirtualInstrument	Input	boolean	False	Flag to create virtual instrument.
DetectorTableWorkspace	Input	TableWorkspace		Name of table workspace containing all the detectors.
OutputWorkspace	Output	MDEventWorkspace	Mandatory	Name of MDEventWorkspace containing all experimental data.
SourcePosition	Input	dbl list		A vector of 3 doubles for position of source.
SamplePosition	Input	dbl list		A vector of 3 doubles for position of sample.
PixelDimension	Input	dbl list		A vector of 8 doubles to determine a cubic pixel’s size.
IsBaseName	Input	boolean	True	It is specified as true if the data file names listed in InputWorkspace are base name without directory.
BackgroundWorkspace	Input	MatrixWorkspace		Name of optional background workspace.
Directory	Input	string		Directory where data files are if InputWorkspace gives data file name as the base file name as indicated by ‘IsBaseName’.

Description ¶

This algorithms is to convert an experiment done on reactor-based four-circle instrument (such as HFIR HB3A) to a MDEventWorkspace with each MDEvent in momentum space.

In this algorithm’s name, ConvertCWSDToMomentum, CW stands for constant wave (reactor-source instrument); SD stands for single crystal diffraction.

This algorithm takes ??? as inputs.

Futhermore, the unit of the output matrix workspace can be converted to momentum transfer (Q).

Outline of algorithm ¶

Create output workspace

Build a virtual instrument, requiring

position of source

position of sample

detector ID, position, detector size of pixels

Read in data via table workspace

From each row, (1) file name and (2) starting detector ID are read in.

Detector position in (virtual) instrument of MDEventWorkspace is compared with the position in MatrixWorkspace

Momentum is calcualted by goniometry values

Input Workspaces ¶

Two TableWorkspaces, which contain experiment information, are required.

InputWorkspace is a TableWorkspace containing the data files names to be loaded for the experiment. It is required to have 4 columns. They are Scan, Pt, Filename and StartDetID respectively.

A typical HB3A experiment consists of multiple scans, each of which contains multiple measurement point (i.e., Pt). FileName is the XML data file for 2D detector information for a specific Scan/Pt pair. StartDetID is the starting detector ID of a specific Scan/Pt mapped to the virtual instrument.

DetectorTableWorkspace is a TableWorkspace that list the parameters of all detector pixels belonged to the virtual instrument. The parameters include detector ID in virtual instrument, detector’s position in cartesian coordinate, and detector’s original detector ID.

Outputs ¶

The output is an MDEventWorkspace that stores the data of an HB3A experiment. Every non-zero count recorded by detector is converted to an MDEvent in the output workspace.

MDEvent ¶

Each MDEvent in output MDEventWorkspace contain * Kx * Ky * Kz * Signal * Error * Detector ID * Run Number

Combine Experiment Into One MDEventWorkspace ¶

One typical HB3A (reactor-based four-circle diffractometer) experiment consists of a set of scans, each of which contains multiple experiment point (labeled as Pt. in SPICE).

Each experiment point is independent to the others. They can have various detector positions, goniometer setup and even sample environment setup.

In order to integrate them into an organized data structure, i.e., MDEventWorkspace, a virtual instrument is built in the algorithm.

Virtual instrument ¶

A virtual instrument is built in the algorithm. In this virtual instrument, the number of detectors and their position are determined by the number of individual detector’s positions in the experiment.

MDEventWorkspace ¶

There is only one virtual instrument and N ExperimentInfo. N is the total number of experiment points in the experiment.

Inconsistency between using virtual instrument and copying instrument ¶

It is found that the results, i.e., the peak’s position in sample-momentum space, by FindPeaksMD, are different betweent the MDEventWorkspaces output by this algorithm with copying instrument or creating virtual instrument.

It is caused by the difference of the instruments in the MDEventWorkspace. The native HB3A’s detector is of type RectangularDetector, while the virtual instrument’s detector is just of class ComAssembly.

FindPeaksMD calculates the centre of the mass for peak centre, and then locates the nearest pixel of the peak center and re-define the position of the pixel as peak center.

For virtual instrument, CompAssembly::testIntersectionWithChildren() is used to find the pixel’s position; while for rectangular detector, RectangularDetector::testIntersectionWithChildren() is used. Due to the difference in algorithm, there is slightly difference between the position of the pixel found.

Use cases ¶

It is found that creating an instrument with tens of thousands detectors is very time consuming in Mantid. It is caused by creating a map upon these detectors. With this generation of Mantid, there is no simple solution for it.

For HB3A, there are usually $2\theta$ scan, $\omega$ scan and $\phi$ . Only the $2\theta$ scan requires to create virtual instrument, while the MDEventWorkspace can be created by copying instrument instance from parent MatrixWorkspace for the other type of scans.

Therefore, it is suggested to do $\omega$ and $\phi$ scans for HB3A with 2D angular detector.

Usage ¶

Example - convert an HB3A’s experiment to MDWorkspace in sample momentum workspae and creating virtual instrument

# Create input table workspaces for experiment information and virtual instrument parameters
CollectHB3AExperimentInfo(ExperimentNumber='355', ScanList='11', PtLists='-1,11',
    DataDirectory='',
    GenerateVirtualInstrument = True,
    OutputWorkspace='ExpInfoTable', DetectorTableWorkspace='VirtualInstrumentTable')

# Convert to MDWorkspace
ConvertCWSDExpToMomentum(InputWorkspace='ExpInfoTable', DetectorTableWorkspace='VirtualInstrumentTable',
    CreateVirtualInstrument = True,
    OutputWorkspace='QSampleMD', SourcePosition='0,0,2', SamplePosition='0,0,0', PixelDimension='1,2,2,3,3,4,3,3',
    Directory='')

# Find peak in the MDEventWorkspace
FindPeaksMD(InputWorkspace='QSampleMD', DensityThresholdFactor=0.10000000000000001,
    OutputWorkspace='PeakTable')

# Examine
mdws = mtd['QSampleMD']
print('Output MDEventWorkspace has {} events.'.format(mdws.getNEvents()))
peakws = mtd['PeakTable']
print('There are {} peaks found in output MDWorkspace'.format(peakws.getNumberPeaks()))
peak = peakws.getPeak(0)
qsample = peak.getQSampleFrame()
print('In Q-sample frame, center of peak 0 is at ({:.5f}, {:.5f}, {:.5f}) at detector with ID {}'.
    format(qsample.X(), qsample.Y(), qsample.Z(), peak.getDetectorID()))

Output:

Output MDEventWorkspace has 397 events.
There are 1 peaks found in output MDWorkspace
In Q-sample frame, center of peak 0 is at (-6.93624, -0.08360, 8.16733) at detector with ID 35723

Example - convert an HB3A experiment to MDEventWorkspace by copying instrument.:

# Create input table workspaces for experiment information and virtual instrument parameters
CollectHB3AExperimentInfo(ExperimentNumber='355', ScanList='11', PtLists='-1,11',
    DataDirectory='',
    GenerateVirtualInstrument=False,
    OutputWorkspace='ExpInfoTable', DetectorTableWorkspace='VirtualInstrumentTable')

# Convert to MDWorkspace
ConvertCWSDExpToMomentum(InputWorkspace='ExpInfoTable',
    CreateVirtualInstrument = False,
    OutputWorkspace='QSampleMD', Directory='')

# Find peak in the MDEventWorkspace
FindPeaksMD(InputWorkspace='QSampleMD', DensityThresholdFactor=0.10000000000000001,
    OutputWorkspace='PeakTable')

# Examine
mdws = mtd['QSampleMD']
print('Output MDEventWorkspace has {} events.'.format(mdws.getNEvents()))
peakws = mtd['PeakTable']
print('There are {} peaks found in output MDWorkspace'.format(peakws.getNumberPeaks()))
peak = peakws.getPeak(0)
qsample = peak.getQSampleFrame()
print('In Q-sample frame, center of peak 0 is at ({:.5f}, {:.5f}, {:.5f}) at detector with ID {}'.
    format(qsample.X(), qsample.Y(), qsample.Z(), peak.getDetectorID()))

Output:

Output MDEventWorkspace has 397 events.
There are 1 peaks found in output MDWorkspace
In Q-sample frame, center of peak 0 is at (-3.58246, -4.40802, -3.06320) at detector with ID 32881

Categories: Algorithms | Diffraction\ConstantWavelength | DataHandling\Text

Source ¶

C++ source: ConvertCWSDExpToMomentum.cpp (last modified: 2017-05-09)

C++ header: ConvertCWSDExpToMomentum.h (last modified: 2016-11-21)