SliceMDHisto dialog.
Table of Contents
| Name | Direction | Type | Default | Description |
|---|---|---|---|---|
| InputWorkspace | Input | IMDHistoWorkspace | Mandatory | |
| Start | Input | int list | A comma separated list of min,for each dimension | |
| End | Input | int list | A comma separated list of max for each dimension | |
| OutputWorkspace | Output | IMDHistoWorkspace | Mandatory |
SliceMDHisto extracts a hyperslab of data from a MDHistoWorkspace. Beyond the usual input and output workspace parameters, the start and end of the hyperslabs dimensions are required. Both as comma separated lists with an entry for each dimension of the MDHistoWorkspace.
Example: consider an input MDHistoWorkspace with dimensions 100,100,100. Running SliceMDHisto with parameters Start= 20,20,20 and End= 50,50,100 will copy all the data between x: 20-50, y: 20-50, z:20-100 into the result MDHistoWorkspace with dimensions 30,30,80.
For a more up-to-date way of performing slices on a MDHistoWorkspace this see IntegrateMDHistoWorkspace v1
Example - Taking an MDHisto slice
def outputMDDimensions(ws):
num_dims = ws.getNumDims()
print "Name Bins Min Max"
for dim_index in range(num_dims):
dim = ws.getDimension(dim_index)
print "%s %i %.2f %.2f" % (dim.getName(),
dim.getNBins(), dim.getMinimum(), dim.getMaximum())
#create a test MD event workspace
mdew = CreateMDWorkspace(Dimensions=3, Extents=[-1,1,-5,5,-9,10],
Names='A, B, C', Units='U, U, U')
FakeMDEventData(mdew, PeakParams=[100000, 0, 0, 0, 1])
#convert to a MDHisto workspace suing BinMD
wsHisto = BinMD(mdew,AlignedDim0='A,-1,1,9',
AlignedDim1='B,-5,5,5',
AlignedDim2='C,-9,10,9')
print "The original workspace"
outputMDDimensions(wsHisto)
#The values in start and end are the Bin numbers(staring at 0) of the dimensions
wsOut = SliceMDHisto(wsHisto,Start=[1,2,0],End=[7,4,7])
print "\nAfter Slicing"
outputMDDimensions(wsOut)
The original workspace
Name Bins Min Max
A 9 -1.00 1.00
B 5 -5.00 5.00
C 9 -9.00 10.00
After Slicing
Name Bins Min Max
A 6 -0.78 0.56
B 2 -1.00 3.00
C 7 -9.00 5.78
Categories: Algorithms | MDAlgorithms