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

LRReductionWithReference v1¶

Summary ¶

REFL reduction using a reference measurement for normalization

Properties ¶

Name	Direction	Type	Default	Description
RunNumbers	Input	str list		List of run numbers to process
InputWorkspace	Input	Workspace		Optionally, we can provide a workspace directly
NormalizationRunNumber	Input	number	0	Run number of the normalization run to use
SignalPeakPixelRange	Input	int list	123,137	Pixel range defining the data peak
SubtractSignalBackground	Input	boolean	True	If true, the background will be subtracted from the data peak
SignalBackgroundPixelRange	Input	int list	123,137	Pixel range defining the background. Default:(123,137)
NormFlag	Input	boolean	True	If true, the data will be normalized
NormPeakPixelRange	Input	int list	127,133	Pixel range defining the normalization peak
SubtractNormBackground	Input	boolean	True	If true, the background will be subtracted from the normalization peak
NormBackgroundPixelRange	Input	int list	127,137	Pixel range defining the background for the normalization
LowResDataAxisPixelRangeFlag	Input	boolean	True	If true, the low resolution direction of the data will be cropped according to the lowResDataAxisPixelRange property
LowResDataAxisPixelRange	Input	int list	115,210	Pixel range to use in the low resolution direction of the data
LowResNormAxisPixelRangeFlag	Input	boolean	True	If true, the low resolution direction of the normalization run will be cropped according to the LowResNormAxisPixelRange property
LowResNormAxisPixelRange	Input	int list	115,210	Pixel range to use in the low resolution direction of the normalizaion run
TOFRange	Input	dbl list	0,340000	TOF range to use
TOFRangeFlag	Input	boolean	True	If true, the TOF will be cropped according to the TOF range property
QMin	Input	number	0.05	Minimum Q-value
QStep	Input	number	0.02	Step size in Q. Enter a negative value to get a log scale
AngleOffset	Input	number	0	angle offset (degrees)
AngleOffsetError	Input	number	0	Angle offset error (degrees)
OutputWorkspace	Output	MatrixWorkspace	Mandatory	Output workspace
ApplyScalingFactor	Input	boolean	True	If true, the scaling from Scaling Factor file will be applied
ScalingFactorFile	Input	string		Scaling factor configuration file
SlitTolerance	Input	number	0.02	Tolerance for matching slit positions
SlitsWidthFlag	Input	boolean	True	Looking for perfect match of slits width when using Scaling Factor file
IncidentMediumSelected	Input	string		Incident medium used for those runs
GeometryCorrectionFlag	Input	boolean	False	Use or not the geometry correction
FrontSlitName	Input	string	S1	Name of the front slit
BackSlitName	Input	string	Si	Name of the back slit
TOFSteps	Input	number	40	TOF step size
CropFirstAndLastPoints	Input	boolean	True	If true, we crop the first and last points
ApplyPrimaryFraction	Input	boolean	False	If true, the primary fraction correction will be applied
PrimaryFractionRange	Input	int list	117,197	Pixel range to use for calculating the primary fraction correction.
Refl1DModelParameters	Input	string		JSON string for Refl1D theoretical model parameters

Description ¶

The workflow proceeds as follows:

Using the algorithm LiquidsReflectometryReduction, reduce the normalization run for a standard using normalization input parameters: \(I^{standard}(Q)\)
With the input Refl1DModelParameters JSON string, calculate the model reflectivity for the normalization run to produce the theoretical reflectivity of the standard. Uses the refl1d [1] package: \(R_{theory}^{standard}(Q)\)
The reduced normalization run from step (1), \(I^{standard}(Q)\), is then divided by the model reflectivity of the same material from step (2), \(R_{theory}^{standard}(Q)\), to produce the incident flux for normalzing the sample run: \(I_{norm}(Q) = I^{standard}(Q) / R_{theory}^{standard}(Q)\).
Using the algorithm LiquidsReflectometryReduction, reduce the sample run with the normalization turned OFF (i.e. NormFlag set to False): \(I^{sample}(Q)\)
Calculate the sample reflectivity by dividing the sample reduction of step (4), \(I^{sample}(Q)\), by the normalization in step (3), thus \(R^{sample}(Q) = I^{sample}(Q) / I_{norm}(Q)\).