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NormaliseByThickness dialog.
Table of Contents
| Name | Direction | Type | Default | Description |
|---|---|---|---|---|
| InputWorkspace | Input | MatrixWorkspace | Mandatory | |
| OutputWorkspace | Output | MatrixWorkspace | Mandatory | Name of the workspace that will contain the normalised data |
| SampleThickness | Input | number | 0 | Optional sample thickness value. If not provided the sample-thickness run property will be used. |
| OutputMessage | Output | string | Output message |
Normalise detector counts by the sample thickness.
#create a workspace
raw=CreateSampleWorkspace()
#apply algorithm
norm=NormaliseByThickness(raw,SampleThickness=10)
#do a quick check
print(norm[1])
print("Min(raw)= {}".format(raw.dataY(0).min()))
print("Min(norm)= {}".format(norm[0].dataY(0).min()))
print("Max(raw)= {}".format(raw.dataY(0).max()))
print("Max(norm)= {}".format(norm[0].dataY(0).max()))
Output:
Normalised by thickness [10 cm]
Min(raw)= 0.3
Min(norm)= 0.03
Max(raw)= 10.3
Max(norm)= 1.03
Categories: AlgorithmIndex | Workflow\SANS
Python: NormaliseByThickness.py (last modified: 2020-03-27)