\(\renewcommand\AA{\unicode{x212B}}\)

ConvertWANDSCDtoQ v1

ConvertWANDSCDtoQ dialog.

Summary

Convert the output of LoadWANDSCD to Q or HKL

See Also

LoadWANDSCD, ConvertHFIRSCDtoMDE

Properties

Name	Direction	Type	Default	Description
InputWorkspace	Input	IMDHistoWorkspace	Mandatory	Input Workspace
NormalisationWorkspace	Input	IMDHistoWorkspace		Workspace to use for normalisation
UBWorkspace	Input	Workspace		Workspace containing the UB matrix to use
Wavelength	Input	number	1.488	Wavelength to set the workspace
S1Offset	Input	number	0	Offset to apply (in degrees) to the s1 of the input workspace
NormaliseBy	Input	string	Monitor	Normalise to monitor, time or None. Allowed values: [‘None’, ‘Time’, ‘Monitor’]
Frame	Input	string	Q_sample	Selects Q-dimensions of the output workspace. Allowed values: [‘Q_sample’, ‘HKL’]
Uproj	Input	dbl list	1,0,0	Defines the first projection vector of the target Q coordinate system in HKL mode
Vproj	Input	dbl list	0,1,0	Defines the second projection vector of the target Q coordinate system in HKL mode
Wproj	Input	dbl list	0,0,1	Defines the third projection vector of the target Q coordinate system in HKL mode
BinningDim0	Input	dbl list	-8.02,8.02,401	Binning parameters for the 0th dimension. Enter it as acomma-separated list of values with theformat: ‘minimum,maximum,number_of_bins’.
BinningDim1	Input	dbl list	-0.82,0.82,41	Binning parameters for the 1st dimension. Enter it as acomma-separated list of values with theformat: ‘minimum,maximum,number_of_bins’.
BinningDim2	Input	dbl list	-8.02,8.02,401	Binning parameters for the 2nd dimension. Enter it as acomma-separated list of values with theformat: ‘minimum,maximum,number_of_bins’.
KeepTemporaryWorkspaces	Input	boolean	False	If True the normalization and data workspaces in addition to the normalized data will be outputted
ObliquityParallaxCoefficient	Input	number	1	Geometrical correction for shift in vertical beam position due to wide beam.
OutputWorkspace	Output	Workspace	Mandatory	Output Workspace

Description

This algorithm will convert the output of LoadWANDSCD v1 in either Q or HKL space. FindPeaksMD v1 can be run on the output Q sample space, then the UB can be found and used to then convert to HKL. The default binning ranges are good for converting to Q sample with the default wavelength.

This algorithm will also work for data from DEMAND (HB3A).

The normalization is calculated in the same way as MDNormSCD v1 but with the solid angle and flux coming from the NormalisationWorkspace, normally vanadium. A brief introduction to the multi-dimensional data normalization can be found here.

When converting to HKL it will use the UB matrix from the UBWorkspace if provided otherwise it will use the UB matrix from the InputWorkspace. Uproj, Vproj and Wproj are only used when converting to HKL Frame.

If the KeepTemporaryWorkspaces option is True the data and the normalization in addition to the nomalized data will be outputted. This allows you to run separate instances of ConvertWANDSCDtoQ and combine the results. They will have names “ws_data” and “ws_normalization” respectively.

Usage

Convert to Q

# Load Data and normalisation
LoadWANDSCD(IPTS=7776, RunNumbers=26509, OutputWorkspace='norm',Grouping='4x4') # Vanadium
LoadWANDSCD(IPTS=7776, RunNumbers='26640-27944', OutputWorkspace='data',Grouping='4x4')
ConvertWANDSCDtoQ(InputWorkspace='data',
                  NormalisationWorkspace='norm',
                  OutputWorkspace='Q',
                  BinningDim1='-1,1,1')

# Plot workspace
import matplotlib.pyplot as plt
from mantid import plots
fig, ax = plt.subplots(subplot_kw={'projection':'mantid'})
c = ax.pcolormesh(mtd['Q'], vmax=1)
cbar=fig.colorbar(c)
cbar.set_label('Intensity (arb. units)')
#fig.savefig('ConvertWANDSCDtoQ_Q.png')

Output:

Convert to HKL

# Load Data and normalisation
LoadWANDSCD(IPTS=7776, RunNumbers=26509, OutputWorkspace='norm',Grouping='4x4') # Vanadium
LoadWANDSCD(IPTS=7776, RunNumbers='26640-27944', OutputWorkspace='data',Grouping='4x4')
SetUB('data', UB='0,0.1770619741,-0.00927942487,0.177304965,0,0,0,-0.00927942487,-0.177061974')
ConvertWANDSCDtoQ(InputWorkspace='data',
                  NormalisationWorkspace='norm',
                  OutputWorkspace='HKL',
                  Frame='HKL',
                  BinningDim0='-1,1,1',
                  BinningDim1='-2.02,7.02,226',
                  BinningDim2='-6.52,2.52,226')

# Plot workspace
import matplotlib.pyplot as plt
from mantid import plots
fig, ax = plt.subplots(subplot_kw={'projection':'mantid'})
c = ax.pcolormesh(mtd['HKL'], vmax=1)
cbar=fig.colorbar(c)
cbar.set_label('Intensity (arb. units)')
#fig.savefig('ConvertWANDSCDtoQ_HKL.png')

Output:

Categories: AlgorithmIndex | DataHandling\Nexus

Source

Python: ConvertWANDSCDtoQ.py