ReplicateMD dialog.
Table of Contents
This is an 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.
The ShapeWorkspace and DataWorkspace can have any number of extra integrated trailing dimensions. For example the ShapeWorkspace can have shape [10, 5, 1, 1] (where each number in the list is a number of bins in the corresponding dimension) and the DataWorkspace can have either [10, 1, 1, 1] or [1, 5, 1, 1]. But the following arrangement will cause a run-time error: shape [10, 1, 5, 1], data [1, 1, 5, 1]. In such case the dimensions can be re-arranged using TransposeMD v1 algorithm.
Example - ReplicateMD 1D to 2D
import numpy as np
data = CreateMDHistoWorkspace(2, SignalInput=np.arange(20*30), ErrorInput=np.arange(20*30), NumberOfEvents=np.arange(20*30), Extents=[-10, 10, -1,1], NumberOfBins=[20, 30], Names='E,Qx', Units='MeV,A^-1')
shape = CreateMDHistoWorkspace(3, SignalInput=np.tile([1], 20*30*10), ErrorInput=np.tile([1], 20*30*10), NumberOfEvents=np.tile([1], 20*30*10), Extents=[-1,1, -10, 10, -10,10], NumberOfBins=[30,20,10], Names='Qx,E,Qy', Units='A^-1, MeV, A^-1')
replicated = ReplicateMD(ShapeWorkspace=shape, DataWorkspace=data)
print('Num dims: {}'.format(replicated.getNumDims()))
print('Num points: {}'.format(replicated.getNPoints()))
Output:
Num dims: 3
Num points: 6000
Categories: Algorithms | MDAlgorithms\Creation
C++ source: ReplicateMD.cpp (last modified: 2016-07-14)
C++ header: ReplicateMD.h (last modified: 2016-06-14)