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FakeMDEventData v1

Summary

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

See Also

CreateMDWorkspace, EvaluateMDFunction

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.
EllipsoidParams	Input	dbl list		Add an nd ellipsoidal peak with a multivariate normal distribution around a central point (x1,…x_N). The ellipsoid is defined by an N eigenvectors with N elements and N eigenvalues which correpsond to the variance along the principal axes. The final argument is doCounts which can be either negative or positive. If doCounts<0 then all events have a count of 1, else if doCounts>0 then the counts of each event is determined by the Mahalanobis distance https://en.wikipedia.org/wiki/Mahalanobis_distance Parameters: number_of_events, x_1,..,x_N, eigvect_1(1),…,eigvect_1(N), …, eigvect_N(1),..eigvect_N(N), eigval_1,…,eigval_N, doCounts.
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:

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:

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:

Categories: AlgorithmIndex | MDAlgorithms\Creation

Source

C++ header: FakeMDEventData.h

C++ source: FakeMDEventData.cpp