SmoothMD dialog.
Table of Contents
| Name | Direction | Type | Default | Description |
|---|---|---|---|---|
| InputWorkspace | Input | IMDHistoWorkspace | Mandatory | An input MDHistoWorkspace to smooth. |
| WidthVector | Input | int list | Mandatory | Width vector. Either specify the width in n-pixels for each dimension, or provide a single entry (n-pixels) for all dimensions. |
| Function | Input | string | Hat | Smoothing function. Defaults to Hat. Allowed values: [‘Hat’] |
| InputNormalizationWorkspace | Input | IMDHistoWorkspace | Multidimensional weighting workspace. Optional. | |
| OutputWorkspace | Output | IMDHistoWorkspace | Mandatory | An output smoothed MDHistoWorkspace. |
Provides smoothing of MDHistoWorkspace in n-dimensions. The WidthVector relates to the number of pixels to include in the width for each dimension. WidthVector must contain entries that are odd numbers.
A InputNormalizationWorkspace may optionally be provided. Such workspaces must have exactly the same shape as the InputWorkspace. Where the signal values from this workspace are zero, the corresponding smoothed value will be NaN. Any un-smoothed values from the InputWorkspace corresponding to zero in the InputNormalizationWorkspace will be ignored during neighbour calculations, so effectively omitted from the smoothing altogether. Note that the NormalizationWorkspace is not changed, and needs to be smoothed as well, using the same parameters and InputNormalizationWorkspace as the original data.
No smoothing
Smooth with WidthVector=5
Example - SmoothMD
ws = CreateMDWorkspace(Dimensions=2, Extents=[-10,10,-10,10], Names='A,B', Units='U,U')
FakeMDEventData(InputWorkspace=ws, PeakParams='100000,-5,0,1')
FakeMDEventData(InputWorkspace=ws, PeakParams='100000,5,0,1')
histogram = BinMD(InputWorkspace=ws, AlignedDim0='A,-10,10,50', AlignedDim1='B,-10,10,50', OutputExtents='-10,10,-10,10,-10,10', OutputBins='10,10,10')
# plotSlice(histogram)
smoothed = SmoothMD(InputWorkspace=histogram, WidthVector=5, Function='Hat')
# plotSlice(smoothed)
print 'Smoothed has %i points' % smoothed.getNPoints()
Output:
Smoothed has 2500 points
Categories: Algorithms | MDAlgorithms\Transforms