ReplicateMD dialog.
Table of Contents
This is a algorithm to create a higher dimensional dataset by replicating along an additional axis
| Name | Direction | Type | Default | Description |
|---|---|---|---|---|
| ShapeWorkspace | Input | IMDHistoWorkspace | Mandatory | An input workspace defining the shape of the output. |
| DataWorkspace | Input | IMDHistoWorkspace | Mandatory | An input workspace containing the data to replicate. |
| OutputWorkspace | Output | IMDHistoWorkspace | Mandatory | An output workspace with replicated data. |
This algorithm creates a higher dimensional dataset by replicating along an additional axis. The synax is similar to that used by Horace.
The ShapeWorkspace input defines the shape of the OutputWorkspace, but not the contents. The DataWorkspace provides the data contents in the lower dimensionality cut, which will be replicated over. This algorithm operates on MDHistoWorkspace inputs and provides a MDHistoWorkspace as an output.
Example - ReplicateMD 1D to 2D
import numpy as np
data = CreateMDHistoWorkspace(1, SignalInput=np.arange(100), ErrorInput=np.arange(100), NumberOfEvents=np.arange(100), Extents=[-10, 10], NumberOfBins=[100], Names='E', Units='MeV')
shape = CreateMDHistoWorkspace(2, SignalInput=np.tile([1], 10000), ErrorInput=np.tile([1], 10000), NumberOfEvents=np.tile([1], 10000), Extents=[-1,1, -10, 10], NumberOfBins=[100,100], Names='Q,E', Units='A^-1, MeV')
replicated = ReplicateMD(ShapeWorkspace=shape, DataWorkspace=data)
print 'Num dims:', replicated.getNumDims()
print 'Num points:', replicated.getNPoints()
Output:
Num dims: 2
Num points: 10000
Categories: Algorithms | MDAlgorithms