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HFIRGoniometerIndependentBackground v1¶
Summary¶
Generates a background from a 3 dimensional MDHistoWorkspace.
Properties¶
Name |
Direction |
Type |
Default |
Description |
|---|---|---|---|---|
InputWorkspace |
Input |
IMDHistoWorkspace |
Mandatory |
Input workspace, must be a 3 dimensional MDHistoWorkspace |
BackgroundLevel |
Input |
number |
50 |
Backgound level defines percentile range, (default 50, median filter) |
BackgroundWindowSize |
Input |
number |
Optional |
Background Window Size, only applies to the rotation axis, assumes the detectors are already grouped. Integer value or -1 for All values |
FilterMode |
Input |
string |
nearest |
Mode should be ‘nearest’ if the rotation is incomplete or ‘wrap’ if complete within a reasonable tolerance. Allowed values: [‘nearest’, ‘wrap’] |
OutputWorkspace |
Output |
Mandatory |
Description¶
This algorithm is used to generate a background for HFIR monochromatic diffraction data. This algorithm wraps Scipy.ndimage.percentile_filter to generate the background for the input workspace. In the case that BackgroundWindowSize is -1, Numpy.percentile is used to generate the background as it is much faster.
Usage¶
# create workspace
import numpy as np
signal = np.random.randint(low=0, high=10, size=(100,100,100))
workspace = CreateMDHistoWorkspace(SignalInput=signal,
ErrorInput=np.ones_like(signal),
Dimensionality=3,
Extents='0,10,0,10,0,10',
Names='x,y,z',
NumberOfBins='100,100,100',
Units='number,number,number',
OutputWorkspace='output')
# Perform the background interpolation
outputWS = HFIRGoniometerIndependentBackground(workspace, BackgroundWindowSize=10)
# Check output
print("Shape of the resulting Signal is: {}".format(outputWS.getSignalArray().shape))
Output:
Shape of the resulting Signal is: (100, 100, 100)
Categories: AlgorithmIndex | Diffraction\Reduction | Diffraction\Utility