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LRReductionWithReference v1

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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

long

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

Categories: AlgorithmIndex | Reflectometry\SNS

Source

Python: LRReductionWithReference.py