FakeMDEventData v1

../_images/FakeMDEventData-v1_dlg.png

FakeMDEventData dialog.

Summary

Adds fake multi-dimensional event data to an existing MDEventWorkspace, for use in testing. You can create a blank MDEventWorkspace with CreateMDWorkspace.

Properties

Name Direction Type Default Description
InputWorkspace InOut MDEventWorkspace Mandatory An input workspace, that will get DataObjects added to it
UniformParams Input dbl list   Add a uniform, randomized distribution of events. 1 parameter: number_of_events; they will be distributed across the size of the workspace. Depending on the sign of this parameter, the events are either distributed randomly around the box (Case 1, positive) or placed on the regular grid through the box (Case 2, negative) Treatment of Multiple parameters: depends on the Case Case 1: number_of_events, min,max (for each dimension); distribute the events inside the range given. Case 2: Additional parameters describe initial location and steps of the regular grid in each dimension
PeakParams Input dbl list   Add a peak with a normal distribution around a central point. Parameters: number_of_events, x, y, z, ..., radius.
RandomSeed Input number 0 Seed int for the random number generator.
RandomizeSignal Input boolean False If true, the events’ signal and error values will be randomized around 1.0+-0.5.

Description

For testing MDEventWorkspaces, this algorithm either creates a uniform, random distribution of events, or generates regular events placed in boxes, or fills peaks around given points with a given number of events.

Usage

This algorithm can be run on a pre-existing MDEventWorkspace or a newly created one. All of the examples below will be done with newly created ones using CreateMDWorkspace.

2D Uniform Event Distribution Example

ws = CreateMDWorkspace(Dimensions='2', EventType='MDEvent', Extents='-10,10,-10,10',
                       Names='Q_lab_x,Q_lab_y', Units='A,B')
FakeMDEventData(ws, UniformParams="1000000")
print("Number of events = {}".format(ws.getNEvents()))

Output:

Number of events = 1000000

The output looks like the following in the SliceViewer:

Uniform 2D FakeMDEventData

3D Peaks Example

Creates 3 peaks in (H,K,L) at (0,0,0), (1,1,0) and (1,1,1).

ws = CreateMDWorkspace(Dimensions='3', Extents='-3,3,-3,3,-3,3', Names='h,k,l',
                       Units='rlu,rlu,rlu', SplitInto='4')
FakeMDEventData(ws, PeakParams='10000,1,1,1,0.1', RandomSeed='63759', RandomizeSignal='1')
FakeMDEventData(ws, PeakParams='100000,0,0,0,0.1', RandomSeed='63759', RandomizeSignal='1')
FakeMDEventData(ws, PeakParams='40000,1,1,0,0.1', RandomSeed='63759', RandomizeSignal='1')
print("Number of events = {}".format(ws.getNEvents()))

Output:

Number of events = 150000

The output looks like the following in the SliceViewer:

Peaks 3D FakeMDEventData

Running BinMD on the resulting workspace, the output looks like the following in the VSI:

Peaks 3D FakeMDEventData in VSI

4D Peaks Example

Creates a peak at (H,K,L) of (0,0,0) around T=5K.

ws = CreateMDWorkspace(Dimensions='4', Extents='-1,1,-1,1,-1,1,0,10', Names='H,K,L,T', Units='rlu,rlu,rlu,K',
                       SplitInto='2', SplitThreshold='50')
FakeMDEventData(ws, PeakParams='1e+06,0,0,0,5,0.2', RandomSeed='3873875')
print("Number of events = {}".format(ws.getNEvents()))

Output:

Number of events = 1000000

The output looks like the following in the SliceViewer when the T slider is moved to ~5K:

Peaks 4D FakeMDEventData

Running BinMD on the resulting workspace to create a 3D workspace with L integrated, the output looks like the following in the VSI:

Peaks 4D FakeMDEventData as 3D in VSI

Categories: AlgorithmIndex | MDAlgorithms\Creation

Source

C++ source: FakeMDEventData.cpp (last modified: 2019-06-04)

C++ header: FakeMDEventData.h (last modified: 2018-10-05)