6.1

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LRReductionWithReference v1

../_images/LRReductionWithReference-v1_dlg.png

LRReductionWithReference dialog.

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 long 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 long list 123,137 Pixel range defining the background. Default:(123,137)
NormFlag Input boolean True If true, the data will be normalized
NormPeakPixelRange Input long 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 long 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 long 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 long 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 long 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:

  1. Using the algorithm LiquidsReflectometryReduction, reduce the normalization run for a standard using normalization input parameters: \(I^{standard}(Q)\)
  2. 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: \(R_{theory}^{standard}(Q)\)
  3. 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: \(I_{norm}(Q) = I^{standard}(Q) / R_{theory}^{standard}(Q)\).
  4. Using the algorithm LiquidsReflectometryReduction, reduce the sample run with the normalization turned OFF (i.e. NormFlag set to False): \(I^{sample}(Q)\)
  5. Calculate the sample reflectivity by dividing the sample reduction of step (4), \(I^{sample}(Q)\), by the the normalization in step (3), thus \(R^{sample}(Q) = I^{sample}(Q) / I_{norm}(Q)\).

References

[1]Refl1D is a program for analyzing 1D reflectometry measurements made with X-ray and neutron beamlines. Refl1D GitHub repo and Refl1D Docs

Categories: AlgorithmIndex | Reflectometry\SNS

Source

Python: LRReductionWithReference.py (last modified: 2020-07-08)